GraphAgent: Agentic Graph Language Assistant

Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social connections and user behaviors) and implicit interdependencies among semantic entities, often illustrated through knowledge graphs. In this work, we propose GraphAgent, an automated agent pipeline that addresses both explicit graph dependencies and implicit graph-enhanced semantic inter-dependencies, aligning with practical data scenarios for predictive tasks (e.g., node classification) and generative tasks (e.g., text generation). GraphAgent comprises three key components: (i) a Graph Generator Agent that builds knowledge graphs to reflect complex semantic dependencies; (ii) a Task Planning Agent that interprets diverse user queries and formulates corresponding tasks through agentic self-planning; and (iii) a Task Execution Agent that efficiently executes planned tasks while automating tool matching and invocation in response to user queries. These agents collaborate seamlessly, integrating language models with graph language models to uncover intricate relational information and data semantic dependencies. Through extensive experiments on various graph-related predictive and text generative tasks on diverse datasets, we demonstrate the effectiveness of our GraphAgent across various settings. We have made our proposed GraphAgent open-source at: https://github.com/HKUDS/GraphAgent.

Learning Geospatial Region Embedding with Heterogeneous Graph

Learning effective geospatial embeddings is crucial for a series of geospatial applications such as city analytics and earth monitoring. However, learning comprehensive region representations presents two significant challenges: first, the deficiency of effective intra-region feature representation; and second, the difficulty of learning from intricate inter-region dependencies. In this paper, we present GeoHG, an effective heterogeneous graph structure for learning comprehensive region embeddings for various downstream tasks. Specifically, we tailor satellite image representation learning through geo-entity segmentation and point-of-interest (POI) integration for expressive intra-regional features. Furthermore, GeoHG unifies informative spatial interdependencies and socio-environmental attributes into a powerful heterogeneous graph to encourage explicit modeling of higher-order inter-regional relationships. The intra-regional features and inter-regional correlations are seamlessly integrated by a model-agnostic graph learning framework for diverse downstream tasks. Extensive experiments demonstrate the effectiveness of GeoHG in geo-prediction tasks compared to existing methods, even under extreme data scarcity (with just 5% of training data). With interpretable region representations, GeoHG exhibits strong generalization capabilities across regions. We will release code and data upon paper notification.

Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing. Specifically, we first delve into data perspective to comprehend the role of each modality and data source. Secondly, we classify the methodology into four primary categories: feature-based, alignment-based, contrast-based, and generation-based fusion methods. Thirdly, we further categorize multi-modal urban applications into seven types: urban planning, transportation, economy, public safety, society, environment, and energy. Compared with previous surveys, we focus more on the synergy of deep learning methods with urban computing applications. Furthermore, we shed light on the interplay between Large Language Models (LLMs) and urban computing, postulating future research directions that could revolutionize the field. We firmly believe that the taxonomy, progress, and prospects delineated in our survey stand poised to significantly enrich the research community. The summary of the comprehensive and up-to-date paper list can be found at https://github.com/yoshall/Awesome-Multimodal-Urban-Computing.