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Xue-Min Lin

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Jun 2026

Efficient Hyper-Truss Decomposition over Hypergraphs

Cohesive subgraph mining in hypergraphs has recently attracted increasing research attention due to its broad applicability in domains such as social networks, co-authorship networks, and recommendation systems. An important model, the hyper k -truss, is defined as a maximal cohesive subgraph in which each hyper-ed...

Hao-Zhe Yin, Kai Wang, Wen-Jie Zhang et al. · 0 citations
Open access Sep 2026

Efficient and cost-effective influence and blocker minimization

Online social networks have emerged as prominent platforms for individuals to rapidly share ideas and perspectives. However, their swift dissemination capabilities also make them powerful channels for the spread of misinformation. Such dissemination results in substantial economic and societal harm, highlighting the ne...

Jing-Hao Wang, Yan-Ping Wu, Xiao-Yang Wang et al. · 0 citations
Book Open access Aug 2026

Structure-Aware Abstraction of Hierarchical Time Series

This work provides the first formal analysis of HTSA, proving its NP-hardness and showing that the objective is neither monotone nor submodular, and proposes OSS, whose discretized search provides a 1/alpha-approximation guarantee for each single optimal-subtree computation.

Yihan Wu, Xuliang Zhu, Guozhong Li et al. · 0 citations
Book Open access Aug 2026

Mitigating Anomaly Hallucination: A Model-Agnostic Framework for Unsupervised Anomaly Detection on Dynamic Graphs

The proposed AHEAD, an unsupervised anti-hallucination anomaly detection framework featuring a hallucination refinement pipeline and a temporal-structural detector, which is compatible with various T-GNN backbones for reliable anomaly detection on CTDGs.

Yingxuan Li, Yuanyuan Xu, Xue-Min Lin et al. · 0 citations
Book Aug 2026

Mitigating Anomaly Hallucination: A Model-Agnostic Framework for Unsupervised Anomaly Detection on Dynamic Graphs

Temporal graph neural networks (T-GNNs) are powerful for representation learning on continuous-time dynamic graphs (CTDGs), but naively applying them to unsupervised anomaly detection is often unreliable. The key challenge is learning normal dynamics while being exposed to unlabeled anomalies, which destabilizes optimi...

Yingxuan Li, Yuanyuan Xu, Xuemin Lin et al. · 0 citations
#artificial intelligence Review Jun 2026

Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective

This survey model agent state as a dynamic graph, where memories, tools, skills, workflows, and inter-agent relations are represented as typed nodes, edges, and subgraphs updated through schema-constrained rewrites to provide a compact structural lens for designing and governing self-evolving agents.

Yuanyuan Xu, Wenjie Zhang, Yin Chen et al. · 2 citations

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