Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved significant accomplishments, these models still fall short of I) investigating the impact of multi-granular interactions across recent snapshots, and II) harnessing the expressive semantics of significant links accorded with queries throughout the entire history, particularly events exerting a profound impact on the future. These inadequacies restrict representation ability to reflect historical dependencies and future trends thoroughly. To overcome these drawbacks, we propose an innovative TKG reasoning approach towards Historically Relevant Events Structuring (HisRES). Concretely, HisRES comprises two distinctive modules excelling in structuring historically relevant events within TKGs, including a multi-granularity evolutionary encoder that captures structural and temporal dependencies of the most recent snapshots, and a global relevance encoder that concentrates on crucial correlations among events relevant to queries from the entire history. Furthermore, HisRES incorporates a self-gating mechanism for adaptively merging multi-granularity recent and historically relevant structuring representations. Extensive experiments on four event-based benchmarks demonstrate the state-of-the-art performance of HisRES and indicate the superiority and effectiveness of structuring historical relevance for TKG reasoning.

Understanding and predicting urban dynamics is crucial for managing transportation systems, optimizing urban planning, and enhancing public services. While neural network-based approaches have achieved success, they often rely on task-specific architectures and large volumes of data, limiting their ability to generalize across diverse urban scenarios. Meanwhile, Large Language Models (LLMs) offer strong reasoning and generalization capabilities, yet their application to spatial-temporal urban dynamics remains underexplored. Existing LLM-based methods struggle to effectively integrate multifaceted spatial-temporal data and fail to address distributional shifts between training and testing data, limiting their predictive reliability in real-world applications. To bridge this gap, we propose UrbanMind, a novel spatial-temporal LLM framework for multifaceted urban dynamics prediction that ensures both accurate forecasting and robust generalization. At its core, UrbanMind introduces Muffin-MAE, a multifaceted fusion masked autoencoder with specialized masking strategies that capture intricate spatial-temporal dependencies and intercorrelations among multifaceted urban dynamics. Additionally, we design a semantic-aware prompting and fine-tuning strategy that encodes spatial-temporal contextual details into prompts, enhancing LLMs' ability to reason over spatial-temporal patterns. To further improve generalization, we introduce a test time adaptation mechanism with a test data reconstructor, enabling UrbanMind to dynamically adjust to unseen test data by reconstructing LLM-generated embeddings. Extensive experiments on real-world urban dynamics datasets from multiple cities demonstrate the effectiveness of UrbanMind. The results consistently show that UrbanMind outperforms state-of-the-art baselines, achieving superior accuracy and strong generalization, even in zero-shot scenarios with no prior data.

Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model’s ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches.

Contextual information parsing is one of the most important subtasks of conversational KBQA. However, existing methods often assume the independence of utterance and model them in isolation. In this paper, we propose a Dependency paRsing‐Enhanced converSational queStion AnswerinG systEm, DRESSAGE, which can effectively model long‐range semantic dependencies in the conversation history. This is a multitask neural semantic parsing model. The model can perform explicit dependency parsing for several history questions and the current question and enhance the entity recognition module and the question encoding module with the parsing tree. The performance of the DRESSAGE model is tested on the widely used CSQA dataset and gets SOTA in the overall effect, which proves the effectiveness of this model.

Multi-view graph clustering has been intensively investigated during the past years. However, existing methods are still limited in two main aspects. On the one hand, most of them can not deal with data that have both attributes and graphs. Nowadays, multi-view attributed graph data are ubiquitous and the need for effective clustering methods is growing. On the other hand, many state-of-the-art algorithms are either shallow or deep models. Shallow methods may seriously restrict their capacity for modeling complex data, while deep approaches often involve large number of parameters and are expensive to train in terms of running time and space needed. In this paper, we propose a novel multi-view attributed graph clustering (MAGC) framework, which exploits both node attributes and graphs. Our novelty lies in three aspects. First, instead of deep neural networks, we apply a graph filtering technique to achieve a smooth node representation. Second, the original graph could be noisy or incomplete and is not directly applicable, thus we learn a consensus graph from data by considering the heterogeneous views. Third, high-order relations are explored in a flexible way by designing a new regularizer. Extensive experiments demonstrate the superiority of our method in terms of effectiveness and efficiency.

Graph clustering is a prevalent issue associated with social networks, data mining, and machine learning; its objective is to detect communities or groups in networks. Inspired by the recent success of deep learning (DL), new DL-based graph clustering methods have achieved promising results. However, a deep neural network involves a large number of training parameters. Moreover, existing methods typically select the similarity metric by an ad hoc approach, which considerably affects the resulting output. In this study, we propose a principled graph learning perspective, fine-grained attributed graph clustering. Based on a shallow approach, the proposed method sufficiently exploits both node features and structure information by benefiting from graph convolution. Consequently, a fine-grained graph encoded higher-order relations is automatically learned. Comprehensive experiments on benchmark datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms, including several DL methods.

Subgraph matching query is to find out the sub-graphs of data graph G which match a given query graph Q. Traditional methods can not deal with big data graphs due to their high computational complex. In this paper, we propose a distributed top-k subgraph search method over big graphs. The proposed method is designed at the level of single vertex and all vertices obtain their matching state separately without requiring global graph information. Therefore, it can be easily deployed in distributed platform like Hadoop. The evaluations of running time, number of messages and supersteps show the efficiency and scalability of the proposed method.