Xiaobao Wang

The conversational recommendation system (CRS) breaks the limitations of traditional static methods, presenting a novel framework for personalized recommendations with real-time adaptability and dynamic interactions. Existing methods predominantly focus on the balance between ”exploration and exploitation (E&E)”, but they struggle to explore sufficiently in large-scale candidate sets and rely heavily on extensive historical interaction data to support accurate decision-making. Consequently, these methods often face issues such as instability in recommendations and poor adaptation to new users in cold-start scenarios. To address these challenges, we propose an innovative meta-learning-driven conditional diffusion model to replace E&E. Specifically, we use a generative model to directly model the data distribution and learn personalized user patterns, enhancing recommendation stability. Meanwhile, the meta-learning framework implicitly constrains the optimization process of the diffusion model and other components, enhancing the model's ability to rapidly adapt to new users and dynamic preferences. Extensive experimental results demonstrate that the proposed method outperforms representative baseline methods on several real-world datasets, validating its effectiveness and robustness.

Graph Anomaly Detection (GAD) is critical in applications such as fraud prevention, cybersecurity, and social governance. While Graph Neural Networks (GNNs) have achieved remarkable success in detecting anomalies on homogeneous graphs, they face fundamental challenges in real-world heterogeneous settings involving diverse node types and imbalanced semantic richness. In heterogeneous graphs, nodes often vary significantly in semantic richness, with anomalies potentially spanning multiple types and emerging implicitly through cross-type dependencies. We identify two core limitations of existing methods: (i) the ineffective propagation of discriminative anomaly cues from informative to sparse nodes due to semantic imbalance, and (ii) conflicting optimization objectives arising from joint detection across multiple node types. To address these issues, we propose CSA-MTHGAD, a novel framework that integrates smoothness-guided cross-type semantic alignment with dynamic multi-task learning. It selectively propagates anomaly-sensitive features across types and harmonizes task-specific gradients through adaptive projection and weighting.To facilitate research, we employ two real-world heterogeneous benchmarks in the domain of social governance. Extensive experiments demonstrate that CSA-MTHGAD achieves superior performance over state-of-the-art baselines in accuracy, robustness, and generalization for multi-type anomaly detection.

Clustering is a fundamental task in graph data mining, including both node-level and graph-level clustering. While the former has been extensively explored to capture local structures and features, the latter has gained attention for its ability to capture global relationships and high-level abstractions. However, existing methods often address these two tasks in isolation, which not only wastes computational resources but also fails to fully leverage the knowledge from both levels to improve each other, hindering consistent performance improvement. To this end, we propose a novel Unified Graph Clustering Network called UGCN, which employs both local and global graph information to address node- and graph-level clustering collaboratively. In detail, we design a dual-branch projector that performs joint learning at both node and graph levels. The first branch extracts node-level features and projects them into distinct cluster layers, where the derived prototypes are used to refine graph attributes and highlight clustering-friendly substructures. In parallel, the second branch captures subgraph embeddings and aggregates them into discriminative graph-level representations. we align the two branches through joint contrastive objectives to establish a bidirectional interaction: refined prototypes guide subgraph and graph-level clustering, while graph-level pseudo-labels provide feedback to enhance node-level clustering. Extensive experimental results across seven datasets demonstrate that our method significantly outperforms existing state-of-the-art approaches.

Graph Out-of-Distribution (OOD) detection aims to identify whether a test graph deviates from the distribution of graphs observed during training, which is critical for ensuring the reliability of Graph Neural Networks (GNNs) when deployed in open-world scenarios. Recent advances in graph OOD detection have focused on test-time training techniques that facilitate OOD detection without accessing potential supervisory information (e.g., training data). However, most of these methods employ a one-pass inference paradigm, which prevents them from progressively correcting erroneous predictions to amplify OOD signals. To this end, we propose a \textbf{S}elf-\textbf{I}mproving \textbf{G}raph \textbf{O}ut-\textbf{o}f-\textbf{D}istribution detector (SIGOOD), which is an unsupervised framework that integrates continuous self-learning with test-time training for effective graph OOD detection. Specifically, SIGOOD generates a prompt to construct a prompt-enhanced graph that amplifies potential OOD signals. To optimize prompts, SIGOOD introduces an Energy Preference Optimization (EPO) loss, which leverages energy variations between the original test graph and the prompt-enhanced graph. By iteratively optimizing the prompt by involving it into the detection model in a self-improving loop, the resulting optimal prompt-enhanced graph is ultimately used for OOD detection. Comprehensive evaluations on 21 real-world datasets confirm the effectiveness and outperformance of our SIGOOD method. The code is at https://github.com/Ee1s/SIGOOD.

Sarcasm functions as a distinct mode of communication, intended to convey a meaning contrary to its literal interpretation. With the rapid proliferation of social networks, various manifestations of multimodal sarcasm have become widespread. Consequently, there is a growing emphasis on discerning sarcasm conveyed through multimodal data. Existing research often provides only a superficial interpretation of images, lacking a thorough exploration of the contextual nuances embedded within, particularly in understanding the scene depicted in the image. In this paper, we aim to delve deeper into the information embedded within images. Specifically, we begin by extracting entity relationships from images to capture the contextual information they convey. Additionally, we utilize image text recognition to extract textual information from the images. After conducting a comprehensive analysis of the image information, we establish consistency modeling between the image content and text using external knowledge. Finally, we employ a graph neural network to process the constructed cross-modal graph and make predictions regarding sarcasm. Extensive experiments validate the state-of-the-art performance of our model on publicly available multimodal Twitter datasets.

The widespread proliferation of fake news on the Internet, especially in multi-modal formats, poses a substantial threat to society. Most deep learning-based approaches for fake news detection yield accurate predictions but lack explainability. Existing models focusing on explainability visualize key components from results or generate surface causes via Large Language Models. However, they can hardly provide the deep rationale behind the fabrication of fake news, which is indispensable for misinformation mitigation. Thus, we approach explainability from a different perspective, focusing on explaining how fake news is fabricated, which we term deceptive patterns, at its very source. First, four types of deceptive patterns are pre-established, namely Image Manipulation, Cross-modal Inconsistency, Image Repurposing and Others. Based on this, we propose GE-NSLM, a General Explainable Neuro-Symbolic Latent Model that integrates the power of Large Vision Language Models, which not only provides accurate judgments but also offers insights on deceptive patterns. Specifically, each deceptive pattern is represented as a binary learnable latent variable, obtained through amortized variational inference and weak supervision guided by logical rules. Experiments show GE-NSLM achieves competitive performance. More importantly, it provides interpretable insights into the underlying reasons why specific news items are fake.