Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing how complex real-world systems evolve over time. However, existing methods are predominantly black-box models. They lack interpretability in their clustering decisions and fail to provide semantic explanations of why clusters form or how they evolve, severely limiting their use in safety-critical domains such as healthcare or transportation. To address these limitations, we propose an end-to-end interpretable framework that maps continuous graph embeddings into discrete semantic concepts through learnable prototypes. Specifically, we first decompose node representations into orthogonal role and clustering subspaces, so that nodes with similar roles (e.g., hubs, bridges) but different cluster affiliations can be properly distinguished. We then introduce five node role prototypes (Leader, Contributor, Wanderer, Connector, Newcomer) in the role subspace as semantic anchors, transforming continuous embeddings into discrete concepts to facilitate LLM understanding of node roles within communities. Finally, we design a hierarchical LLM reasoning mechanism to generate both clustering results and natural language explanations, while providing consistency feedback as weak supervision to refine node representations. Experimental results on four synthetic and six real-world benchmarks demonstrate the effectiveness, interpretability, and robustness of DyG-RoLLM. Code is available at https: //github.com/Clearloveyuan/DyG-RoLLM.

Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and recommendation accuracy. Existing debiasing methods mitigate popularity bias often uniformly across all users and only partially consider the time evolution of users or items. However, users have different levels of preference for item popularity, and this preference is evolving over time. To address these issues, we propose a novel method called CausalEPP (Causal Intervention on Evolving Personal Popularity) for taming recommendation bias, which accounts for the evolving personal popularity of users. Specifically, we first introduce a metric called Evolving Personal Popularity to quantify each user's preference for popular items. Then, we design a causal graph that integrates evolving personal popularity into the conformity effect, and apply deconfounded training to mitigate the popularity bias of the causal graph. During inference, we consider the evolution consistency between users and items to achieve a better recommendation. Empirical studies demonstrate that CausalEPP outperforms baseline methods in reducing popularity bias while improving recommendation accuracy.

In the field of multi-document summarization (MDS), transformerbased models have demonstrated remarkable success, yet they suffer an input length limitation.Current methods apply truncation after the retrieval process to fit the context length; however, they heavily depend on manually well-crafted queries, which are impractical to create for each document set for MDS.Additionally, these methods retrieve information at a coarse granularity, leading to the inclusion of irrelevant content.To address these issues, we propose a novel retrieval-based framework that integrates query selection and document ranking and shortening into a unified process.Our approach identifies the most salient elementary discourse units (EDUs) from input documents and utilizes them as latent queries.These queries guide the document ranking by calculating relevance scores.Instead of traditional truncation, our approach filters out irrelevant EDUs to fit the context length, ensuring that only critical information is preserved for summarization.We evaluate our framework on multiple MDS datasets, demonstrating consistent improvements in ROUGE metrics while confirming its scalability and flexibility across diverse model architectures.Additionally, we validate its effectiveness through an in-depth analysis, emphasizing its ability to dynamically select appropriate queries and accurately rank documents based on their relevance scores.These results demonstrate that our framework effectively addresses context-length constraints, establishing it as a robust and reliable solution for MDS. 1 * Both authors contributed equally to this research.

Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix factorization-based methods are promising approaches for this task; however, these methods often struggle with scalability and can be time-consuming when applied to large-scale dynamic graphs. Moreover, they tend to lack robustness and are vulnerable to real-world noisy data. To address these issues, we make three key contributions. First, to improve scalability, we propose temporal separated matrix factorization, where a single matrix is divided into multiple smaller matrices for independent factorization, resulting in faster computation. Second, to improve robustness, we introduce bi-clustering regularization, which jointly optimizes graph embedding and clustering, thereby filtering out noisy features from the graph embeddings. Third, to further enhance effectiveness and efficiency, we propose selective embedding updating, where we update only the embeddings of dynamic nodes while the embeddings of static nodes are fixed among different timestamps. Experimental results on six synthetic and five real-world benchmarks demonstrate the scalability, robustness and effectiveness of our proposed method. Source code is available at https://github.com/Clearloveyuan/DyG-MF.

Abstract Trajectories serve as a cornerstone of intelligent transportation systems, playing an important role in many applications such as traffic flow prediction, route planning, and urban management. However, the availability of such data is limited due to privacy issues, ethical concerns, and the high cost associated with infrastructure deployment. In recent years, rapidly developing generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs) have demonstrated strong capabilities in learning complex data distributions and generating synthetic data, thereby alleviating the data accessibility issue. In this survey, we systematically review the existing literature on deep generative models that address the problem of trajectory generation. First, we classify the existing literature into two categories: unconditional and conditional trajectory generation. In unconditional generation, trajectories are generated without contextual constraints, whereas conditional generation incorporates several important factors such as road network topology, time of day, and user preferences to guide the trajectory generation process. Then, for each category, we further classify the literature into three methodological types, including VAEs, GANs, and DMs, and analyze how these models address key challenges under different settings. Finally, we discuss promising directions for future research and hope to inspire further advances in trajectory generation.

With the booming development of social media, temporal link prediction (TLP), as a core technology, has been receiving increasing attention. However, current methods are based on graph neural networks, which suffer from the over-smoothing issue and easily yield indistinguishable node representations, degrading the prediction accuracy. Besides, they lack the ability to eliminate noisy temporal information and ignore the importance of high-order neighbor information for measuring the link probability between nodes. To solve these issues, we design a cross-view graph contrastive learning (GCL) framework for TLP, called Tacl. We first design two augmented views for GCL by enhancing the temporal and topological information to obtain distinguishable node representations. Then, we learn the evolution rule of temporal networks to help constrain consistency of node representations and eliminate noise. Finally, we incorporate the high-order neighbor information to measure the link probability between nodes. Extensive experiments demonstrate the effectiveness and robustness of Tacl.

Multi-modal personality traits recognition aims to recognize personality traits precisely by utilizing different modality information, which has received increasing attention for its potential applications in human-computer interaction. Current methods almost fail to extract distinguishable features, remove noise, and align features from different modalities, which dramatically affects the accuracy of personality traits recognition. To deal with these issues, we propose an emotion-guided multi-modal fusion and contrastive learning framework for personality traits recognition. Specifically, we first use supervised contrastive learning to extract deeper and more distinguishable features from different modalities. After that, considering the close correlation between emotions and personalities, we use an emotion-guided multi-modal fusion mechanism to guide the feature fusion, which eliminates the noise and aligns the features from different modalities. Finally, we use an auto-fusion structure to enhance the interaction between different modalities to further extract essential features for final personality traits recognition. Extensive experiments on two benchmark datasets indicate that our method achieves state-of-the-art performance and robustness.

Graph contrastive learning (GCL) methods aim to learn more distinguishable representations by contrasting positive and negative samples. They have received increasing attention in recent years due to their wide application in recommender systems and knowledge graphs. However, almost all GCL methods are applied to static networks and can not be extended to temporal networks directly. Furthermore, recent GCL models treat low- and high-frequency nodes equally in overall training objectives, which hinders the prediction precision. To solve the aforementioned problems, in this paper, we propose a Temporality- and Frequency-aware Graph Contrastive Learning for temporal networks (TF-GCL). Specifically, to learn more diverse representations for infrequent nodes and fully explore temporal information, we first generate two augmented views from the input graph based on topological and temporal perspectives. We then design a temporality and frequency-aware objective function to maximize the agreement between node representations of the two views. Experimental results demonstrate that TF-GCL remarkably achieves more robust node representations and significantly outperforms the state-of-the-art methods on six temporal link prediction benchmark datasets. Considering the reproducibility, we release our code on Github.