Thanks to the exceptional ability of diffusion models to capture complex patterns, many diffusion-based models have achieved significant success in spatiotemporal prediction. Noise plays a crucial role in diffusion models, directly impacting the quality of predictions. However, these models typically do not leverage spatiotemporal prior knowledge to guide noise prediction, relying solely on the model to learn and fit the noise distribution. This approach often results in temporal jitter and global inconsistencies in the predicted data, failing to fully capture the intrinsic patterns of spatiotemporal data. Inspired by recent advances in video generation tasks, we aim to utilize spatiotemporal prior knowledge to adjust the noise during the diffusion process. By doing so, we can effectively guide the diffusion noise prediction process with spatiotemporal knowledge, thereby enhancing the model's predictive capabilities.

Behavior prediction is pivotal for understanding human actions and societal patterns. Traditional models frequently encounter issues such as data intensity and poor generalization. Leveraging Large Language Models (LLMs) addresses these challenges by requiring less data, enhancing generalization, and improving interpretability through comprehensive world knowledge. Our study integrates sequential models with LLM embeddings using a projector to unify event ID embeddings from sequential models with the LLM's input space. This transition from pure text prompts to hybrid prompts significantly enhances predictive accuracy. Our approach underscores the potential of LLMs to advance behavior prediction tasks.

Embodied intelligence can experience, learn, and accumulate knowledge from its environment, developing its own intelligence. The learning paradigm of embodied intelligence is similar to that of humans, making it a significant research direction towards achieving artificial general intelligence (AGI). Training embodied intelligence requires complex and diverse environments to enable the acquisition of generalizable capabilities. Most existing research on embodied intelligence is based on simple indoor settings or limited artificially constructed outdoor environments. Even those claiming to train in open-world scenarios often do so in game environments with restricted rules and scope. In these simplified environments, embodied intelligence cannot undergo comprehensive learning, necessitating more diverse and complex environments. Traditionally, environments need to be manually constructed, which involves substantial effort to create varied settings. We propose the use of multimodal generative models to automatically generate diverse and complex outdoor environments, facilitating the training of more robust embodied agents.

Rural roads play a crucial role in achieving the Sustainable Development Goals (SDGs) by facilitating access to essential services, promoting economic development, and enhancing the overall quality of life in rural areas. However, research on rural roads remains relatively sparse compared to urban roads. Unlike urban roads, rural roads exhibit considerable surface variability, rapid changes, and severe occlusions. These characteristics make it challenging to use a single model for road segmentation. Therefore, we leverage the analytical and inferential capabilities of multi-modal large models to supplement the segmentation of rural roads. In this presentation, I will summarize the related work and show the proposed segmentation framework.

With the rapid urbanization in China, cities are increasingly facing severe congestion, thereby turning to subways as a solution to alleviate on-road traffic loads. Although subways are regarded as an energy-efficient mode of transportation during operation, their underground construction entails significant material use and labor-intensive tasks, resulting in a carbon cost and questioning the overall climate friendliness of subway systems from a life-cycle perspective. Thus, it is crucial to develop tools that assess the climate efficiency of subways, which is defined as the ability to offset operational carbon emissions and compensate for the carbon debt incurred during construction. In this presentation, I will present the pipeline of subway climate efficiency calculation, and discuss our findings regarding which cities would benefit most from subway systems and how subway lines should be planned.

The goal of daily intent prediction is to forecast a user's next likely intention by analyzing their historical behavior. This approach is valuable for optimizing smartphone systems and enhancing user experiences. There are two primary challenges in this work: firstly, there is currently a lack of a foundation model in user behavioral field, and second, data is unevenly distributed, and long-tail intent modelling is difficult. To address this challenge, we propose a pre-training with fine-tuning framework for large-scale mobile phone users. Specifically, in the pretrain stage, we utilize large-scale user behavior data to train a foundation model, where we design a DRO loss function to improve the performance of the model on long-tail intents. In the finetune stage, we utilize the RAG method to better capture the complexity and personalization of user behavior and utilize the lightweight finetuning to reduce the computational cost while maintaining accuracy for long-tail intents. This is a work in progress, and anyone is welcome to give me suggestions and ask questions at the seminar.

In the era of raising environmental pollution and climate change, solar power is an essential renewable resource to combat air pollution and reduce the usage of fossil fuels. However, the peak of solar power generation occurs at noon, which does not match the city power consumption peak at night. Fortunately, the increasing adoption of electric vehicles (EVs) provides us a possible solution. We can use EV batteries as a giant power storage pool and coordinate the EVs to charge during the power generation peak and discharge during the power consumption peak besides their daily normal usage. The aim is to bridge the gap between solar power generation and city power consumption, maintaining the pulse on the power grid caused by EV charging and discharging with a safe threshold. This requires real-time coordination of the charging and moving behaviors of large amount of EVs in the whole city, limiting the performance of conventional solutions by human experts or operational research. In this work, we propose a multi-agent reinforcement learning (MARL) solution, where the RL agents are aware of the spatial-temporal information of EVs and the power grid through self-supervised representation. Experimental results on the real-world dataset illustrate that our method can reduce fossil energy consumption by millions of kilowatt-hours per day, significantly reducing carbon emissions from the power system. In addition, our approach (is expected to) reduce traffic congestion by coordinating the movement of EVs, thereby reducing carbon emissions from the transportation system at the same time.

Human mobility prediction is crucial in urban planning, emergency management, and traffic optimization. However, current research on mobility prediction focuses on single-type mobility data, such as trajectory or flow data, which limits their broad applicability and practical utility. In reality, different types of mobility data describe mobility behaviors from multiple perspectives, featuring complementary and shared spatiotemporal patterns. Therefore, in this work, we propose a universal human mobility prediction model named UniMob, which is adaptable to various types of mobility data. Specifically, we first utilize diverse mobility data, converting it into spatiotemporal tokens for sequential modeling. Then, we design special training strategies to jointly model trajectory and flow data, effectively integrating information from macroscopic and microscopic perspectives. Extensive experiments on three real-world datasets validate the superiority of our model over state-of-the-art baselines in trajectory and flow prediction, especially in scenarios with limited and noisy data.

With the large-scale integration of renewable energy sources and the increase in electrical loads, their inherent volatility and unpredictability pose challenges to the stability and reliability of the power system. As an emerging energy management platform, the Virtual Power Plant (VPP) enhances overall energy utilization efficiency and economic benefits by integrating distributed energy resources and adjustable load resources. The resources covered by the VPP consist of various types of equipment, each with varying quantities, and uncertainties regarding future electricity prices and renewable energy generation, which complicate optimization decisions. To address these issues, we propose a VPP optimization scheme based on Multi-Agent Reinforcement Learning (MARL). The scheme treats each type of equipment as an agent to accommodate the characteristics of large-scale heterogeneous equipment. Subsequently, to tackle the variability in the number of devices, a scalable network structure design is employed, ensuring the scalability and adaptability of the scheme. Finally, to make more robust decisions in a fluctuating electricity environment, we combine MARL with risk assessment to optimize the reward distribution. Experimental results on the real data demonstrate that our method can better enhance the revenue of the VPP participating in the electricity market.

Predicting the future state of complex dynamical systems not only presents theoretical research value, as in chaotic theories, but also has wide applications in real-world systems such as urban traffic and electricity demand. As system details are often unknown, model-free prediction from their time series data is especially important. Important advances in this area include traditional statistical models and recent booming machine learning models, made by both physicists and AI researchers. However, until now, there have been few approaches that can meet diverse requirements in the dimensions of accuracy, efficiency, interpretability, and generalizability. Is it possible to develop such versatile and powerful models? This presentation will focus on our preliminary investigation towards this objective.

The control of dynamical processes on networks is a challenging task with applications in engineering, biology, and the social sciences. Existing works on the controllability of complex networks have focused exclusively on a model-based setting, although, in practice, constructing accurate models of large-scale networks is a challenging(often unfeasible) task. The knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. With the development of sensing, processing, and storing capabilities of modern sensors, massive volumes of information-rich data can be dynamically generated by complex interconnected processes and encode key information about the structure and operation of these networked phenomena. As a result, we seek to overcome this limitation and propose to leverage offline-RL method to directly learn the network controls from data, and we focus on control policies that optimally steer the state of (a subset of) network nodes from a given initial value to a desired final one within a finite-time horizon. In this talk, we discuss our recent progress in solving the control problem under this formulation. We convert it into a planning problem and propose leveraging diffusion-based offline RL methods, which are recently recognized for their power in planning, to solve our problem. Experiments on simple toy datasets reveal that the diffusion model is capable of generating proper control trajectories for dynamic control. The presentation will focus on our research background and current experimental advancements, and we eagerly welcome valuable insights and discussions from our audience.

Mobile app usage behavior reveals human patterns and is crucial for stakeholders, but data collection is costly and raises privacy issues. Data synthesis can address this by generating artificial datasets that mirror real-world data. In this study, we propose AppGen, an autoregressive generative model designed to generate app usage behavior based on users’ mobility trajectories, improving dataset accessibility and quality. Specifically, AppGen employs a probabilistic diffusion model to simulate the stochastic nature of app usage behavior. By utilizing an autoregressive structure, AppGen effectively captures the intricate sequential relationships between different app usage events. Additionally, AppGen leverages latent encoding to extract semantic features from spatio-temporal points, guiding behavior generation. These key designs ensure the generated behaviors are contextually relevant and faithfully represent users’ environments and past interactions. Experiments with two real-world datasets show that AppGen outperforms baselines by over 12% in critical metrics and accurately reflects real-world spatio-temporal patterns. We also test the generated datasets in applications, demonstrating their suitability for downstream tasks by maintaining algorithm accuracy and order.

The widespread use of recommender systems on online platforms has significantly improved user experience, but it also raises concerns about information cocoons, which narrow user interests and can harm society. In this study, we gathered a dataset of the practical watching behaviors of about 3 million users on Kwai and found that, in terms of profile, senior citizens and users with lower-priced phones have a higher chance of falling into information cocoons. In terms of interest, users who prefer entertainment videos are more likely to fall into cocoons. In terms of behavior, behaviors with low exploration and high exploitation tend to lead to information cocoons. Furthermore, using all the correlated features mentioned above as inputs, we designed a model based on the characteristics of these input features to predict information cocoons after an early observation of user behaviors. This model outperformed the baselines, namely DeepFM and LightGBM.

With the rapid development of 5G and 6G communications, network planning and optimization have become increasingly crucial, such as base station deployment, autonomous network control, and ubiquitous 3D network (space-air-ground-sea integrated network) construction. Generating network traffic data is able to proactively estimate mobile user demands, and provide opportunities for network operators to optimize network performance. However, due to the complex Spatio Temporal (ST) characteristics of mobile communication demands, current small machine learning models show weakness in generating high-fidelity network traffic data. Moreover, they are usually tailored for specific scenarios that require task-specific model designs and cannot be well generalized across multiple communication scenarios. In addition, it is difficult to apply existing methods in those areas lacking historical data for model training. In this paper, we propose a Large Mobile traffic Model (LMM) framework to generate network traffic data, which takes the transformer-plus-diffusion model as the training backbone, and is built upon the pretrain-finetuning paradigm. The LMM utilizes large foundation models’ multimodal data fusion and efficient feature extraction capabilities to effectively capture the correlation between complex mobile network environments and ST network traffic data. Moreover, it possesses in-context inference abilities that leverage ST knowledge-guided prompts to generate customized network traffic data across various service scenarios. We introduce the overall framework of LMM and explore its potential application scenarios, followed by two typical use cases. Finally, we discuss future research directions and challenges.

Fringe communities promoting conspiracy theories and extremist ideologies have thrived on mainstream platforms, raising questions about the mechanisms driving their growth. Here, we hypothesize and study a possible mechanism: new members may be recruited through fringe-interactions: the exchange of comments between members and non-members of fringe communities. We apply text-based causal inference techniques to study the impact of fringe-interactions on the growth of three prominent fringe communities on Reddit: r/Incel, r/GenderCritical, and r/The Donald. Our results indicate that fringe-interactions attract new members to fringe communities. Users who receive these interactions are up to 4.2 percentage points (pp) more likely to join fringe communities than similar, matched users who do not. This effect is influenced by 1) the characteristics of communities where the interaction happens (e.g., left vs. right-leaning communities) and 2) the language used in the interactions. Interactions using toxic language have a 5pp higher chance of attracting newcomers to fringe communities than non-toxic interactions. We find no effect when repeating this analysis by replacing fringe (r/Incel, r/GenderCritical, and r/The Donald) with non-fringe communities (r/climatechange, r/NBA, r/leagueoflegends), suggesting this growth mechanism is specific to fringe communities. Overall, our findings suggest that curtailing fringe-interactions may reduce the growth of fringe communities on mainstream platforms.

Urban renewal has become the focus of urban development as cities enter a deep phase of urbanization. This process involves multiple stakeholders like professionals and local residents, and thus requires participatory planning to better consider their varied interests. However, the traditional participatory paradigm encounters the difficulties of both participation (i.e. low efficiency, high cost, low interest to participate) and planning (i.e. hard to balance the demands of different stakeholders). In this work, we propose an novel participatory urban renewal framework empowered by LLMs. Specifically, we construct LLM agents to simulate planner and local residents, and show that they are equipped with the capabilities of a planner, and can accurately reflect the needs of real residents. Moreover, we design a multi-agent collaboration mechanism to carry out urban renewal draft considering diverse needs of residents. Experiments show that the framework can generate realistic urban renewal draft, which achieves the state-of-the-art performance in terms of resident satisfaction, walk score and inequity metrics. Moreover, the planning process includes substantial resident participation and provides reasonable explanation for the renewal.

Segregation, the separation of individuals from different backgrounds, can severely affect sustainable development and deteriorate social cohesion. Traditional segregation literature focuses on static residential segregation across neighborhoods with different income or racial backgrounds. Recently, emerging mobility data sources have provided a new perspective for studying dynamic segregation in daily encounters. In this talk, I will review the progress made over the past decade on mobility segregation, focusing on the following points: a) how to measure mobility segregation; b) new insights derived from mobility segregation; and c) modeling segregation in daily mobility with mathematical tools. Finally, I will discuss prospects for combining mobility network modeling with the understanding of segregation.

With the rapid development of urban transportation and the continuous advancement in autonomous vehicles, the demand for safely and efficiently testing autonomous driving and traffic optimization algorithms arises, which needs accurate modeling of large-scale urban traffic scenarios. Existing traffic simulation systems encounter two significant limitations. Firstly, they often rely on open-source datasets or manually crafted maps, constraining the scale of simulations. Secondly, vehicle models within these systems tend to be either oversimplified or lack controllability, compromising the authenticity and diversity of the simulations. In this paper, we propose LCSim, a large-scale controllable traffic simulator. LCSim provides map tools for constructing unified high-definition map (HD map) descriptions from open-source datasets including Waymo and Argoverse or publicly available data sources like OpenStreetMap to scale up the simulation scenarios. Also, we integrate diffusion-based traffic simulation into the simulator for realistic and controllable microscopic traffic flow modeling.

In the development of artificial intelligence today, Large Language Models (LLMs) have demonstrated their powerful capabilities in processing, understanding, and generating text. However, these models inherit and may amplify social biases in the data during training. This work aims to explore the impact of workload and the use of Chain of Thought (CoT) on answering biased questions in different situations. We designed multiple sets of comparative experiments to evaluate and compare the performance of the models on multiple benchmarks when facing complex mathematical problems and biased questions, as well as the impact of CoT on model output. We found that in gpt-3.5-turbo, Chain of Thought can significantly reduce biased output. When LLM is restricted in token output, its accuracy in answering complex and biased questions decreases slightly due to increased mental strain.

With the development of the digital economy and the continuous promotion of the construction of a digital China, the importance of data has received increasing attention. Data is listed as a new type of production factor alongside land, labor, capital, and technology. The concept of data elements is proposed to promote the large-scale flow and application of data, in order to deeply integrate data into economic cycles and continuously release value. Efficient and compliant flow of data elements is the core of data value release. This is not only a process of data exchange, but also accompanied by complex commercial activities, involving the transfer of data ownership and usage rights among various entities. Therefore, this article designs a new architecture for data element circulation network, combining content distribution network with blockchain technology, exploring key technologies, and completing the preliminary development and testing of network prototype.

The optimization of base station energy consumption and network coverage is essential for efficient wireless communication. Traditional methods often treat these aspects separately, leading to suboptimal results. We propose an approach using multi-agent reinforcement learning method based on graph neural network to address this multi-objective problem. By aligning transmit power and antenna configuration, our method dynamically adjusts parameters to minimize energy consumption while maintaining high service quality. This integrated approach enhances network efficiency and sustainability, supporting the growing demand for reliable and eco-friendly wireless communication.

Large Language Models (LLMs) are often regarded as black-box models, lacking in their ability to capture and utilize factual knowledge effectively. On the other hand, Knowledge Graphs (KGs) are structured models that explicitly store rich factual information. They have the potential to enhance LLMs by providing external knowledge for inference and improving interpretability. However, constructing and evolving KGs can be challenging, especially when it comes to generating new facts and representing unseen knowledge. Therefore, integrating LLMs and KGs can be seen as a complementary approach, leveraging the strengths of both models to enhance their overall performance.

Large language models(LLMs) with powerful language generation and reasoning capabilities have already achieved success in many domains, e.g., math and code generation. However, due to the lacking of physical world’s corpus and knowledge during training, they usually fail to solve many real-life tasks in the urban space. In this paper, we propose CityGPT, a systematic framework for enhancing the capability of LLMs on understanding urban space and solving the related urban tasks by building a city-scale world model in the model. First, we construct a diverse instruction tuning dataset CityInstruction for injecting urban knowledge and enhancing spatial reasoning capability effectively. By using a mixture of CityInstruction and general instruction data, we fine-tune various LLMs (e.g., ChatGLM3-6B, Qwen1.5 and LLama3 series) to enhance their capability without sacrificing general abilities. To further validate the effectiveness of proposed methods, we construct a comprehensive benchmark CityEval to evaluate the capability of LLMs on diverse urban scenarios and problems. Extensive evaluation results demonstrate that small LLMs trained with CityInstruction can achieve competitive performance with commercial LLMs in the comprehensive evaluation of CityEval.

A person's day is made up of behaviors, each with its own semantics. Predicting human daily behaviors can help us better understand human behavior patterns, social activities, and group interactions. However, in current behavior prediction, pseudo-IDs or statistical information are often used to represent specific behaviors, which can lead to a loss of behavioral information and semantics, resulting in inaccurate behavior prediction. Given the powerful capabilities of large language models in understanding text semantics, we propose a bootstrapping behavior representation learning method based on large language models. By interpreting the semantics and underlying logic of different behaviors through the large language model, and aligning the behavior encoding of the behavior prediction model with the behavior semantic encoding based on the large language model, this method endows the behavior prediction model with additional semantic information, thereby further completing the prediction of human behavior.

Urban morphology, the study of the form and structure of urban spaces, provides significant insights into the dynamics of urban development and the functionality of cities. This presentation will offer a comprehensive review of urban morphology and explore its potential in advancing urban science studies. By focusing on the intricate relationship between building morphology indicators and the broader urban fabric, it highlights innovative methods and metrics for quantifying urban form. We will examine various building morphology indicators, demonstrating how these metrics can be systematically measured and analyzed to interpret overall urban morphology. To integrate urban morphology into urban science studies, we propose a general framework for incorporating urban morphology with the primary research domains of urban science. By transitioning urban morphology from its qualitative and manually-based tradition to a quantitative and scientific approach, we aim to provide new insights for urban studies and possibly open up new research opportunities.

Socioeconomic indicators, such as population density and commercial activity, play a vital role in monitoring progress toward Sustainable Development Goals (SDGs) related to poverty, health, air quality, and more. However, traditional data collection methods, such as in-person surveys, suffer issues from timeliness, resolution, and cost. Despite the challenges, easily accessible urban imagery (including satellite imagery and street view imagery) becomes an important tool for socioeconomic indicator prediction in recent studies. Most existing methods extract visual features from urban imagery, which either fail to accurately explain the contribution of notable urban features in socioeconomic indicator prediction nor cover rich semantic information in urban environment. Considering the strong understanding of large language models, this study uses urban images to train a MultiModal Large Language Model to predict various socioeconomic indicators. The experiments in the United States verify the effectiveness of our initial model.

Health has always been a significant part of scientific research, for its fundamental atrribute and profound affect over the whole human society. Health also includes multiple subfields, including mental health, physical health and social well-being. As the thriving of machine learning, health related research also has been pushed to a new level, millions of medical records can be analyzed by large language models, unreadable medical images can be easily explained by deep learning algorithms. Now we are trying to figure out a new way for health prediction, with the help of satellite and street view images. We have proved the potential of contrastive learning in prediction of mental health, and now with the integration of large language models, a new road for health has been opened.

Dynamical phase transition, characterized by the critical time of the evolving systems, is ubiquitous in non-linear systems. Empirical complex network studies document substantial evidence for the self-organized dynamical phase transition from a sparse network to a dense network, characterized by a power law under the critical point. This suggests the existence of an `uprising' phase, i.e., a blow-up state, resulting from the non-stationary dynamics of networks. However, a comprehensive theoretical framework capturing generic mechanisms governing the non-stationary network dynamics and the emergence of the blow-up phase remains unaddressed. In this paper, we introduce a `friends of friends' model, capturing the non-linear interaction determined by the neighbor of the neighbor in the complex network. Under this generic assumption, we find two different phases, stationary and uprising phases, of the evolving networks. For the uprising phase, our theory analytically predicts a dynamical phase transition and the critical points of the complex network. We find the predicted dynamical phase transition corresponding to a condensation process, a transition from a sparse to a dense network. Our theory not only reproduces the empirically observed power law but also analytically predicts the degree of condensation under the critical point.

Africa, a continent rich in cultural diversity and natural resources, faces a multitude of challenges and opportunities in its journey toward sustainable development. This presentation will provide a comprehensive overview of recent research findings on key aspects of Africa's development, including the economy, urbanization, education, health, environment, and conflict. Drawing from top-tier academic journals, we will highlight significant trends and challenges within these domains. A particular focus will be given to the potential for new research methodologies enabled by globally available satellite imagery. Satellite data offers an unprecedented opportunity to gather detailed and up-to-date information on various aspects of development, even in remote and under-studied areas. This presentation will introduce our preliminary research leveraging nighttime light data to explore the relationship between electricity infrastructure and infant mortality rates across different regions in Africa. Our findings underscore the importance of reliable electricity in improving health outcomes and highlight the disparities in infrastructure that need to be addressed. By showcasing the power of satellite imagery and data-driven research, we aim to inspire new avenues for investigation and collaboration, ultimately contributing to more effective and equitable development strategies in Africa.

We have conducted lots of excllent simulation work based on the urban simulator, and some of them have garnered attention from the Department of Urban Planning. But we acknowledge that there are still areas where our expertise can be further applied. I would like to provides a preliminary review of urban planning problems suitable for simulation, aiming to exploring potential avenues for future collaboration in the field. The tradition simulation methods mainly include cellular automata and ABM, and The topics of interest encompass various aspects, including but not limited to urban expansion, shrinkage city, compact city, land use change, gentrification, slums, housing markets, and commuting. These topics can be roughly divided into two categories: those that focus on land use dynamics and those that focus on human behavior, and each falls within different quadrants based on distinct theoretical assumptions.. While these topics may be well-studied within their field, this review would like to highligh the advances of new simulation techniques, for example physical model or agent-based models, in addressing these complex urban topics.

Generative AI has made notable advances in the image and natural language generation tasks and further inspires potential opportunities in geospatial data generation. Origin-destination (OD) networks are critical in estimating population flows within city regions, thus facilitating related urban applications, such as transportation optimization and urban planning. Despite attention from transportation and data mining, existing works focus on generating OD networks using statistical city characteristics, meanwhile, overlooking the impact of buildings on urban mobility. This study explores a new OD generation problem by utilizing building features in regions. To account for various factors, a diffusion model generates OD flows considering regional features and building characteristics.

During extreme disasters like hurricanes, cold waves, and wildfires, human mobility patterns in urban areas undergo significant changes due to factors like inaccessible transportation, infrastructure damage, and shortages of essential supplies. Different disaster types have varying impacts on human mobility. This study aims to explore common characteristics in the changes of human mobility patterns under different disaster types. A dynamic model of human mobility networks was constructed, describing both node status updates and the evolution of network edges. By identifying functional patterns from empirical data, the dynamic model was refined to simulate the evolution of human mobility networks during disasters, capturing the variations in human mobility patterns before, during, and after disasters. This model not only helps reveal the mechanisms by which disasters affect human mobility patterns but also provides insights into the intrinsic changes in human mobility under extreme disasters.

In the realm of opinion dynamics, mathematical models have yielded invaluable insights into collective viewpoint evolution. Traditional approaches rely on numerical representations, limiting their ability to capture the rich nuances of human language in real-life interactions. Large Language Models (LLMs) have addressed this limitation, enabling language comprehension within algorithms. LLM-agent-based models, representing a new paradigm in behavioral simulations, harness the language comprehension abilities of LLMs to explore opinion dynamics with enhanced fidelity to real-world discourse. In our study, we find that when applied to a binary issue within a random network, LLM-based simulation models are highly likely to produce the polarization phenomenon. We have also delved into investigating the level of realism by attempting to reproduce the stable phase observed in real-world networks. This research aims to unravel how language-driven agents, informed by LLMs, navigate the intricate landscape of opinion dynamics. Our findings shed light on the pronounced emergence and persistence of polarization in simulated scenarios, contributing to a nuanced understanding of collective behavior and societal dynamics. By bridging the gap between mathematical abstraction and linguistic richness, this study not only promises deeper insights into polarized viewpoints but also advances the potential for more realistic simulations in the study of opinion dynamics.

Efficient spatial planning lies at the heart of sustainable urban development, fostering improved accessibility and reduced carbon emissions. Despite the availability of planning support tools, the time-consuming spatial layout task still relies heavily on human experts. But what if we could build an AlphaGo-like model to handle this tedious job, freeing up human designers to unleash their creativity? In this presentation, I will introduce a groundbreaking AI framework for urban planning based on graph neural networks and deep reinforcement learning which automatically lays out urban facilities with superior spatial efficiency, outperforming traditional methods and even human designers with over 3000 times speedup. The framework enables practical applications ranging from achieving 15-minute city to optimizing urban facility locations.

Day-ahead electricity price forecasting has long been one of the most significant problems in the electricity market. Recently, this problem is facing brand new challenges. The rise of carbon neutrality goals globally has accelerated the integration of wind and solar power, leading to heightened uncertainty in electricity prices, more influencing factors, and increasing importance of long-tail scenarios. Consequently, traditional forecasting methods, including machine learning and deep learning models, struggle to adapt to these changes and often fall short of achieving satisfactory accuracy. To address these challenges, we propose a Time-Frequency Transformer-based Diffusion model (TFTDiffusion) with three novel designs. Firstly, we design a conditional diffusion model as the backbone, which is able to characterize the stochastic nature of electricity prices. Secondly, to simultaneously enhance the model's capability of modeling the intricate correlations among large-scale multivariate time series and the escalating uncertainty, we present a time-frequency transformer. Finally, to further enhance the model's performance on long-tail data, we develop a self-adaptive hard sample learning algorithm. Extensive experiments on two large-scale real-world datasets with different characteristics have demonstrated the superior performance of TFTDiffusion, outperforming the state-of-the-art methods by more than 10%. However, TFTDiffusion slightly lack accuracy in predicting some abrupt points in electricity prices. Through testing, we have found that when different diffusion strategies are employed, the model exhibits significantly different predictive characteristics. This has led us to ponder: How can we efficiently and automatically find the optimal diffusion strategy? Research suggests that the Diffusion Schrödinger Bridge (DSB) might be an effective solution. The Schrödinger Bridge problem is a mathematical problem concerning the establishment of the optimal transport between any two distributions. The Simplified Diffusion Schrödinger Bridge model connects the original diffusion model with the Schrödinger Bridge theory, allowing for the automatic optimization of forward and backward diffusion strategies through model training, thereby improving the fitting effect on high-dimensional probability spaces.

With the rapid development and ongoing commercialization of the fifth generation mobile network (5G), telecom operators have the plan to deploy 5G base stations in various regions around the world. Understanding the network demands and user behaviors in a particular unknown area is of great significance for base station deployment. Network traffic generation is a potential approach that enables operators to preemptively estimate network usage in target areas, thereby specifying corresponding deployment strategies to enhance network performance. However, existing methods emerges significant challenges in generating high-fidelity 5G traffic data, since they typically infer the characteristics of 5G traffic in new regions based on the traffic from well-deployed areas, overlooking differences in network usage among different regions. In this paper, we introduce a diffusion model based generative framework to generate 5G network traffic data based on the association between 4G and 5G traffic. The framework utilizes the traffic data from widely deployed 4G base stations to assist the generation model in exploring the 5G traffic patterns in the target area. Through a customized cross-attention mechanism and Graph Convolutional Networks (GCN), our model can capture the temporal-spatial correlation between 4G traffic and 5G traffic. Extensive experiments on real-world datasets have demonstrated that the proposed scheme outperforms existing baselines by over 18.08%. Results of deployment tests demonstrate that our model can meet the network demands of 5G users better.

Despite AI-driven recommendation algorithms being widely adopted to counter information overload, substantial evidence suggests that they are building information cocoons that deprive people of access to diverse information. This leads to a crucial question of whether the formation of information cocoons is disparate among different socioeconomic groups. In this study, we empirically examine a large-scale dataset covering over 3M active users and their complete post-registration history. We find that users with lower income and older ages are more likely to fall into deep information cocoons than the others. Moreover, we discover that these users are characterized by their high reliance on recommendation algorithms and preferential selection behaviors. These behaviors are highly correlated with the formation of deep information cocoons. Therefore, to account for the observed inequality of information cocoons, we will develop a model that incorporates the heterogeneity of user behaviors. This study not only reveals the inequality in information access in the digital ages but also offers insights into mitigating the information cocoons.

The importance of scientific collaborations has been increasingly recognized with the growing dominance of teams in science. Although the merits and drawbacks of collaborations have been pointed to by some existing literature, most of them focus on dyadic interactions, leaving the complicated yet essential high-order structural information underexplored. Leveraging 73 million collaborations formed by 60 million research papers worldwide over forty years, we construct the city-level scientific collaboration network and demonstrate how triads over this network characterize important modular functioning and predict important aspects of scientific collaborations.

Accurately estimating package delivery time is essential to the logistics industry, which enables reasonable work allocation and on-time service guarantee. This becomes even more necessary in mixed logistics scenarios where couriers handle a high volume of delivery and a smaller number of pickup simultaneously. However, most of the related works treat the pickup and delivery patterns on couriers' decision behavior equally, neglecting that the pickup has a greater impact on couriers' decision-making compared to the delivery due to its tighter time constraints. In such context, we have three main challenges: 1) multiple spatiotemporal factors are intricately interconnected, significantly affecting couriers' delivery behavior; 2) pickups have stricter time requirements but are limited in number, making it challenging to model their effects on couriers' delivery process; 3) couriers' spatial mobility patterns are critical determinants of their delivery behavior, but have been insufficiently explored. To deal with these, we propose TransPDT, a transformer-based multi-task package delivery time prediction model. We first employ the transformer encoder architecture to capture the spatio-temporal dependencies of couriers' historical travel routes and pending package sets. Then we design the pattern memory to learn the patterns of pickup in the imbalanced dataset via attention mechanism. We also set the route prediction as an auxiliary task of delivery time prediction, and incorporate the prior courier spatial movement regularities in prediction. Extensive experiments on real industry-scale datasets demonstrate the superiority of our method.

Public transport plays a crucial role in urban mobility, providing accessible and efficient transportation options for the general public. Researchers studying public transport face various challenges, such as resource and cost limitations, highlighting the necessity of utilizing public transport simulations. While simulation frameworks such as MATSim [1], VISSIM [2], and SUMO [3] demonstrate proficiency in simulating the behavior of private vehicles, their coverage of public transport remains limited. MATSim operates as a demand-based traffic simulation prioritizing public transportation as a mode among others like walking and driving, rather than as a functioning system. On the other hand, SUMO and VISSIM offer specialized public transport simulations that incorporate passenger boarding and alighting processes; however, their primary focus remains on the operation of public transport vehicles without offering comprehensive multimodal routing capabilities. To overcome these limitations, we propose a framework based on MObility Simulation System (MOSS) that combines the strengths of these frameworks and incorporates dedicated features for public transport simulation. Our framework can gather global raw data on public transportation (including subway and bus systems) and perform high-speed simulations for both passengers and public transport vehicles. To enable passengers to utilize public transport within the framework, the framework includes a public transport router that calculates the best route in multimodal transportation networks. The proposed public transport framework can obtain data from global geographic scope, acquire navigation results that are close to reality, and simulate at high speeds. These capabilities make it a valuable tool for researchers and practitioners in the field of public transport simulation.

The network dynamics formula describes the behavior of a complex network system in a compact way, which helps to understand the microscopic evolution mechanism of the network. In recent years, the availability of extensive topology and behavior of complex network systems has made it possible to automatically discover the network dynamics formula from empirical data. Here we introduce a network dynamics discovery method that adopts a Neural Network-guided Monte Carlo Tree Search algorithm to search for dynamics formulas described by a set of proposed Network Primitives. This method can efficiently discover networked dynamical formulas, even when there are unknown link weights or heterogeneous parameters. We apply this method to three real-world networked empirical data: microbial population evolution, yeast gene expression, and global epidemic spread, discovering new dynamics formulas that are more accurate than existing ones and have clear physical meanings. These results provide a new path for discovering the microscopic dynamics mechanisms of complex network systems.

Traditional neural network-based traffic forecasting methods cannot conduct large-scale pre-training due to the following reasons: 1) Poor generalization and transferability; 2) Low interpretability, leading to black-box predictions. Fine-tuning methods based on Large Language Models (LLM) combine spatio-temporal modules with partially frozen attention LLM, yet they still require pre-training. Non-fine-tuning methods based on LLM can utilize historical traffic data as prompt inputs for prediction, but they do not consider the spatial dependencies of traffic flow. This paper represents temporal sequences and spatial relationships in natural language, aiming to automatically mine and construct spatial relationships. The goal is to achieve better traffic flow forecasting with smaller training costs.

In this talk, I will present a macroeconomic simulation framework based on an agent-based model at a 1:1 scale [1], where agents are empowered by large language models (LLMs). I'll start with the fundamentals of the model, then delve into the LLM agent design [2, 3]. Utilizing the LLMs' reasoning abilities and human-like traits, along with the collaboration between multiple agents, there are three aspects of experiments to be explored: 1) analyzing macroeconomic policies, optimizing these policies, and forecasting key macroeconomic indicators.

Vehicle emission and air pollution is one of the most important problems in urban sustainability research, concerning the global climate change and human health. In this talk, I will introduce some selected recent studies from “Nature” on this topic that may fall into our interest and strength. The specific research problems include “how to improve vehicle emission computation using big data approach”, “how individual driving styles or platooning driving affect vehicle emissions”, “how the development of compact urban layout or high-speed rail can benefit vehicle emission reduction”, “how electrical vehicle (EV) is related to air quality, how census tract level air quality inequality is brought by unequal EV ownership, and how EV may need several years of use to pay back the emission debt during its manufacture”.

With the escalating integration of renewable energy sources, power storage has emerged as a critical concern for the power grid. Vehicle-to-Grid (V2G) technology presents a promising solution for power storage by facilitating bidirectional power flow between electric vehicle (EV) batteries and the grid. This enables the charging or discharging of EV batteries in accordance with the operational demands of the power grid. This study concentrates on jointly optimizing the charging and discharging schedules of a large EV fleet and their departure times in a V2G framework, aimed at an urban grid with substantial photovoltaic (PV) energy penetration. This integrated approach not only meets the energy storage requirements of the grid but also ensures the prevention of overload in the distributed grid system. The effectiveness of our optimization approach is assessed through simulation scenarios in major cities of China, and preliminary results demonstrate its potential to fulfill the grid's storage requirements.

Navigation in unstructured environments is essential for mobile intelligent systems operating in the physical world. Humans can efficiently navigate unfamiliar environments with just visual cues and language instructions. While previous approaches have employed deep learning or reinforcement learning methods to train agents for the vision-and-language navigation task (VLN), these methods come with high training costs and lack interpretability. Recently, researchers have developed LLM agents for indoor and outdoor navigation. However, these works primarily focus on solving the navigation problem with an instruction following manner, with additional input of language describing a path to the goal. In this work, we propose an autonomous LLM agent for city visual navigation without a map, focusing on translating visual scene semantics into input prompts for the LLM to provide navigational guidance through actions. We fine-tuned LLaVA as the visual encoder and carefully designed the agent architecture to leverage the reasoning and memory abilities of LLM. Through experiments, our agent has achieved better performance than RL agents.

In the critical domains of network environment management and network security, Generating fine-grained packet data based on network statistical information is crucial for developing more powerful and precise network analysis tools, enabling more effective training and testing. The generation of realistic network packet data presents a significant challenge:Restore fine-grained network data based on statistical values in network parameters. To address this challenge, we introduce PacketDiff, an innovative diffusion model designed to generate conditional network packet data based on network statistical data (network flow data). PacketDiff employs a conditional diffusion model that integrates statistical information of network data into a Graph Convolutional Network (GCN)-based conditional mechanism, thereby guiding the generation of more realistic network packet data. The core innovation of PacketDiff lies in its ability to incorporate network statistical information into the fine-grained packet data generation process, achieved through a complex diffusion model with specific conditional mechanisms. This approach differs from traditional methods, which typically rely on simply replicating existing data patterns and fail to capture the minute variations and dynamic nature of network traffic. Extensive experiments with open-source datasets have demonstrated that PacketDiff can closely mimic real network packet data, particularly in key metrics such as packet size and timestamps.

In urban traffic management, Traffic Signal Control (TSC) plays a pivotal role in optimizing road network efficiency and alleviating congestion. However, traditional TSC methods face challenges in adapting to diverse traffic scenarios and lack transparency in decision-making. To address these issues, we introduce LLMLight, a pioneering framework integrating Large Language Models (LLMs) as decision-making agents for TSC. In this presentation, we delve into the details of our approach, elucidating the framework's workflow and the procedure of fine-tuning the backbone LLM. Our methodology harnesses the power of LLMs by providing real-time traffic information as prompts, facilitating a reasoning process akin to human intuition for effective traffic control. Furthermore, we introduce LightGPT, a specialized LLM backbone tailored for TSC tasks, which enhances the LLMLight framework by learning intricate traffic patterns and control strategies. Through extensive experimentation across diverse real-world and synthetic datasets, our proposed method demonstrates the superior effectiveness, generalization capability, and interpretability of LLMLight compared to conventional transportation-based and reinforcement learning baselines.

The widespread adoption of 5G technology has brought about significant changes in people’s lives. In this paper, we aim to study the traffic usage patterns from the perspective of the interaction between 4G and 5G networks. We analyze the variations in user data, traffic, PRB (Physical Resource Block) utilization, capacity, and other metrics at both the network-wide and base station levels. This provides an overview of the overall performance influenced by the coexistence of 4G and 5G. We also explore the user and traffic patterns within the 5G network and the relationship between traffic patterns of 4G and 5G from a temporal perspective. We discover distinct traffic patterns in different functional zones and investigate the underlying reasons for these differences. Moving on to the spatial aspect, we investigate how functional zones and base station types impact the interaction between 4G and 5G. We compare the spatial distribution differences in traffic, users, and base stations between 4G and 5G. Additionally, we explore the causes of these disparities and make predictions regarding future developments. Furthermore, we observe a power-law relationship between 4G and 5G base station density and user density distribution. Based on this, we forecast the evolving trends of 4G networks within the overall network under the influence of 5G. Lastly, we delve into the deployment patterns and development trends of the overall spatial layout of 5G networks under the influence of 4G, considering users, base station types, and traffic.

Segregation is a critical aspect of sustainable urban development, given its profound impact on societal outcomes. Recent research on segregation has shifted from static residential patterns to mobility behavior, using large-scale social data or trajectory data to model individual experienced segregation. The emergence of MM-LLM equipped with the ability to receive and reason with multimodal information has provided a new technological approach for understanding fine-grained urban environmental features from images. In this talk, I try to use the Visual Instruction Tuning Language Model to explore visual cues of experienced segregation from street view images.

Large Language Models (LLMs) are increasingly deployed in embodied environments, such as drones, robots, and simulations, requiring advanced multimodal reasoning capabilities. In this presentation, I will share insights from my literature review on enhancing LLMs' ability to reason across different modalities in embodied settings by incorporating cognitive psychology theories. By bridging cognitive psychology and artificial intelligence, I propose a model framework that integrates cognitive psychology knowledge to improve LLMs' multimodal reasoning skills. The presentation will delve into theories of perception, memory, and multimodal integration, demonstrating their practical application in enhancing LLMs' reasoning abilities and contributing to the development of more intelligent and adaptable AI systems for complex environments.

One of the most prized features of Large Language Models (LLMS) is that they have the ability to generate and understand text in many languages. However, they have been found to be susceptible to bias as a result of the values embedded in the data on which they are trained. What we seek to discover in this work is how much the cultural bias in LLMs is dependent on language, and in turn, whether it is possible to decouple culture from language in the models' output. In turn, we aim to generate a culturally omniscient agent than can transcend multiple cultures while exhibiting a single language persona, which can be used to overcome cultural barriers and facilitate cross-cultural teamwork. In this talk, we illustrate the motivation for such work in the context of existing research, report some preliminary results on the language dependency of culture, and finally, present a roadmap for the realization of a culturally omniscient agent.

Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility [1]. Therefore, we propose an approach that empowers Evolutionary Algorithm (EA) with Large Language Models (LLMs) [2], to generate a function called "score_nodes" which can further be used to identify crucial nodes based on their assigned scores. Our model consists of three main components: Manual Initialization, Population Management, and LLMs-based Evolution. It evolves from initial populations with a set of designed node scoring functions created manually. LLMs leverage their strong contextual understanding and rich programming skills to perform crossover and mutation operations on the individuals, generating excellent new functions [3]. These functions are then categorized, ranked, and eliminated to ensure the stable development of the populations while preserving diversity. Extensive experiments demonstrate the excellent performance of our method, showcasing its strong gener- alization ability compared to other state-of-the-art algorithms. It can consistently and orderly generate diverse and efficient node scoring functions.

Human mobility behaviours are closely linked to various important societal problems. However, collecting mobility data can be prohibitively expensive and involves serious privacy issues, posing a pressing need for high-quality generative mobility models. Previous approaches[1,2,3] based on deep learning or mechanistic models cannot effectively capture the coherent intentions that drive mobility behavior, leading to low sample efficiency and semantic-awareness. In this work, we propose a novel framework that designs a context-aware chain-of-thoughts prompting technique to align LLMs with context-aware mobility behaviour. Besides, we employ a divide-and-coordinate mechanism, which leverages the step-by-step LLM reasoning to recursively generate a temporal template of activity intentions, and then maps them to physical locations with a gravity model. Experiments on two real-world datasets show our framework achieves state-of-the-art performance across all metrics, and substantially reduces the size of training samples at the same time. In the future, we will expand our solution to travel survey and apply it in more interdisciplinary fields.

As the most dominant microscopic traffic simulator in academia, SUMO has been criticized for its poor running speed. However, attempts to improve the operational speed of microscopic traffic simulations, such as CityFlow, have not been able to provide a well-developed tool chain to support road network construction and travel demand generation, and have failed to receive widespread attention and use. Our city simulator, developed over many years, implements a relatively complete tool chain with a high performance version based on GPU acceleration. Therefore, we decide to open source the GPU version of the city simulator under the name MOSS (MObility Simulation System) as a modern alternative of SUMO, launching toolchains including a map generation tool adapted to multiple data sources such as OpenStreetMap, an AIGC-based trip generation tool, a researcher-friendly Python interface to the simulator, and high-performance visualization and editing tools based on modern front-end technologies. We hope that MOSS can become the standard simulator for large-scale microscopic traffic simulations.

While recent progress in multimodal large language models tackles various modality tasks, it’s still unknown of the capabilities for complex urban generation tasks. In this work, we take the initiative to explore a unified multimodal LLM for urban generation, which aligns different modalities (including satellite images, street view images, videos of city scenes and descriptive texts) with a centric LLM. Consequently, the model can interpret inputs and produce outputs in versatile combinations of images, texts and videos. Specifically, the model is expected to achieve urban perception by feeding images or videos as input and generating corresponding texts. Besides, the framework also supports urban view generations given descriptive texts as input. We hope this work can provide a powerful tool for wide urban researches.

Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Traditional theoretical approaches grounded in nonlinear dynamical systems rely on prior knowledge of network dynamics. On the other hand, data-driven approaches frequently encounter the challenge of insufficient labeled data, a predicament commonly observed in real-world scenarios. In this paper, we introduce a novel resilience prediction framework for predicting resilience, designed to tackle this issue through generative data augmentation of network topology and dynamics. The core idea is the strategic utilization of the inherent joint distribution present in unlabeled data, facilitating the learning process of the resilience predictor by illuminating the relationship between network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy of up to 85%-95%. Notably, the framework still demonstrates a pronounced augmented capability in low-data regimes, thereby underscoring its utility and robustness in enhancing the prediction of network resilience.

Urban planning paradigms such as the "15-minute city" are increasingly favored by policymakers due to growing concerns over traffic congestion and emissions. However, existing urban planning methods primarily focus on static mobility data, overlooking the potential impact of changing neighborhood layouts on residents' daily travel trajectories. Furthermore, dynamic large-scale resident mobility simulations based on the layout are too time-consuming, limiting the exploration efficiency of various planning solutions. In this talk, we are going to introduce a novel simulation-decision paradigm for urban planning that generates optimal solutions based on dynamically changing residents' mobilities. Specifically, we present a reward model as a bridge between mobility simulation and intelligent decision-making. Each planning scenario generated by the agent is assessed for its accessibility through the reward module. Moreover, during the iterative process of scheme, the reward module continuously adapts to new planning styles and new data distributions learned from the mobile trajectory simulator to provide accurate performance evaluations of the planning schemes. Preliminary experiments demonstrate the effectiveness of our approach, which significantly reduces the distance traveled by residents to access points of interest (POIs) on a daily basis compared to schemes based solely on static mobility data

The Origin-Destination (OD) matrix, a fundamental data depicting population mobility between regions, underpins critical applications. Despite its significance, traditional methods for OD matrix acquisition are hindered by high costs and extensive data collection requirements, limiting data availability, especially in underdeveloped areas. This research introduces a novel methodology that utilizes publicly available satellite imagery to overcome these limitations, offering a cost-effective and universally applicable solution for global OD matrix generation. By harnessing the capabilities of recent advanced large multimodal models for feature extraction from satellite images, we design a system that generates OD matrices for any target area worldwide. Our method not only broadens the scope of OD matrix generation to encompass developing areas but also demonstrates robustness and universality through extensive experimentation across diverse global locales, including the United States, China, Europe, Japan, Australia, Latin America, and Africa. Experimental results show that satellite imagery can effectively achieve more than 90% of the performance of the traditional paradigm, and the transferability is robust across different areas.

Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future states of nodes on networks, the capability of capturing long-term dynamics remains largely limited. This is because they overlook the fact that long-term dynamics in complex networks are predominantly governed by their inherent low-dimensional manifolds, i.e., skeletons. Therefore, we propose the Dynamics-Invariant Skeleton Neural Network (DiskNet), which identifies skeletons of complex networks based on the renormalization group structure in hyperbolic space to preserve both topological and dynamics properties. Specifically, we first condense complex networks with various dynamics into simple skeletons through physics-informed hyperbolic embeddings. Further, we design graph neural ordinary differential equations to capture the condensed dynamics on the skeletons. Finally, we recover the skeleton networks and dynamics to the original ones using a degree-based super-resolution module. Extensive experiments across three representative dynamics as well as five real-world and two synthetic networks demonstrate the superior performances of the proposed DiskNet, which outperforms the state-of-the-art baselines by 13.78% and 20.80% in terms of MAE and MSE, respectively.

Cascading failure (CF) in infrastructure systems describes the process that the failure of one or some components causes the failure of other components in the network, and then leads to the collapse or breakdown of the whole system. Accurate predictions on CFs are instrumental in ensuring the stable operation of infrastructure and the normal function of cities. CF prediction in existing infrastructures has been widely studied in single networks such as electric networks and road networks, ignoring the diverse and complex dependencies between different infrastructures. Moreover, due to the presence of high-order dynamics and global evolution, capturing the CF dynamics within the same infrastructures is also challenging. In order to solve the above problems, we propose anIntegrated Interdependent Infrastructure cascading failure model (I^3) to capture the cascading failure dynamics within the same infrastructure and between different infrastructures. Specifically, we use a double GAE backbone with global pooling embeddings to capture the dynamics within the same kind of infrastructure, and use a heterogeneous graph structure to capture the dynamics between different kinds of infrastructure. In addition, we add an initial node enhancement pre-training task to avoid the over-smoothing problem caused by the utilized GCN structure. Extensive experiments show that, compared with the state-of-the-art baselines, I^3 can achieve 31.94% improvement in terms of AUC, 18.03% in terms of Precision, 29.17% in terms of Recall, 22.73% in terms of F1-score in predicting which infrastructure will fail, and 28.52% improvement in terms of RMSE in predicting the cascade volume.

Heterogeneous information networks (HIN) have gained increasing popularity in the recent decade for their ability to capture the complex relations between nodes of diverse types. Meta-structure was proposed to identify the important patterns of relations on HIN, which has been proven effective for extracting rich semantic information from HIN and facilitating graph neural networks to learn expressive representations. However, hand-crafted meta-structures pose challenges for scaling up to large and complex HINs, which draw wide research attention for developing automatic meta-structure search algorithms. Previous efforts predominantly concentrate on searching for meta-structures that have good empirical prediction performance, overlooking the explainability of discovered meta-structures. As a result, they often produce complex meta-structures that are prone to overfitting and incomprehensible to human researchers. To address this challenge, we draw inspiration from the emergent reasoning abilities of large language models (LLMs). In this paper, we propose a novel reasoning meta-structure search (ReStruct) framework that integrates LLM reasoning into the evolutionary procedure for meta-structure search. ReStruct uses a grammar translator to encode the meta-structures into natural language sentences, and leverages the reasoning power of LLMs to evaluate the semantically feasible meta-structures. Besides, ReStruct also employs performance-oriented evolutionary operations. These two competing forces allow ReStruct to jointly optimize for semantic explainability and empirical performance of meta-structures. Finally, we also design a differential LLM explainer that can produce natural language explanations for the discovered meta-structures, and refine the explanation by reasoning through the search history. Experiments on five representative HIN datasets demonstrate that ReStruct achieves state-of-the-art performance in both node classification and link recommendation tasks. Additionally, a survey study involving 73 graduate students shows that the meta-structures and natural language explanations generated by ReStruct are substantially more comprehensible.

Pre-trained foundation models have revolutionized Natural Language Processing (NLP) and Computer Vision (CV). However, the field of urban spatio-temporal modeling has not witnessed a parallel breakthrough. Urban spatio-temporal modeling is crucial for various applications such as transportation management, resource optimization, and urban planning. Existing approaches often require task-specific model designs and extensive training data, which largely limits their scalability and universality. In this talk, I will present our recent explorations towards one-for-all solutions in urban spatio-temporal modeling. The main idea is to determine, extract, and leverage universal but underlying commonalities across diverse spatio-temporal data. I will introduce three key strategies: (1) utilization of novel transferable knowledge across cities; (2) spatio-temporal pre-training techniques; (3) effective pattern alignment, organization, and utilization. The proposed one-for-all solutions that achieve these characteristics demonstrate robust generalization across diverse spatio-temporal scenarios, offering a promising direction for advancing the field towards universality.