Attribute-missing deep graph clustering (DGC), which groups graph data containing samples with missing attributes, holds great potential to address the challenges of label scarcity and data absence in real-world scenarios. Among existing efforts, imputation-driven solutions (e.g., AMGC) employ the idea of classical imp...
Wen-Xuan Tu, Jie-Ren Cheng, Ren-Xiang Guan et al.· IEEE Transactions on Pattern...· 0 citations
Detecting hidden confounding is crucial for reliable causal analysis from observational data, directly determining which downstream causal inference method to be deployed. Inspired by the theory of higher-order regression, recent sample-efficient hypothesis testing strategies overcome the restrictive requirement of mul...
Yi-Kai Chen, Hao-Tian Wang, Yunxin Mao et al.· Proceedings of the Thirty-Fi...· 0 citations
This work introduces DGCBench, the first comprehensive and unified benchmark for DGC methods, and develops PyDGC, an open-source Python library that standardizes the DGC training and evaluation paradigm.
Ben-Yu Wu, Yue Liu, Q. Tan et al.· Neural Information Processin...· 5 citations
The results position JuZhou 1.0 as a practical approach to mobile text-to-image generation and provide a concrete reference for Chinese-native generation, domestic-compute training, and fully offline on-device deployment after one-time installation.
Ce Chen, Cong-Rui Wang, Yong-Lin Li et al.· 0 citations
This work proposes Self-Evolving Online Policy Distillation (SeOPD), which enables LLMs to distill and internalize information generated by their own chain of thought (CoT), thereby improving both non-thinking and deep-thinking capabilities.
Xiao-Shu Chen, Si-Hang Zhou, K. Liang et al.· 0 citations
MODAL is a novel multi-modal object re-identification framework, grounded in coupled sparse coding theory and differential suppression principles, that achieves state-of-the-art performance with superior transparency.
Cheng Huang, Jun-Jie Huang, Long Lan et al.· 0 citations
Multi-view Discrete Optimal Transport is instantiated as a cluster-level transport matching framework that first adopts semi-discrete OT to learn a global OT barycenter via aggregating semantic-geometric information from all views, and then transports samples to barycenter-anchored consensus clusters under consistency...
Yuzhuo Dai, Si-Wei Wang, Zhi-Bin Dong et al.· IEEE Transactions on Pattern...· 0 citations
A subgraph-aware semantic expansion module that coordinates $k$-hop neighborhood information with LLM-generated descriptions to alleviate structural sparsity and a contrastive ontology matching mechanism resolves structural inconsistencies by computing adaptive similarity metrics between ontology embeddings and subgrap...
Hao Li, K. Liang, Lingyuan Meng et al.· IEEE Transactions on Pattern...· 0 citations
This work is among the first to introduce and exploit the idea of prompt learning to align HIN features and textual features, and is composed of a text encoder and a graph encoder and uses contrastive learning to align and fuse the graph–text pair.
Yang Fang, Xiang Zhao, Dao-Jian Zeng et al.· ACM Transactions on Informat...· 0 citations
A unified paradigm based on Cross-View Semantic Propagation (CVSP), a plug-and-play framework that effectively improves anchor quality and generalization capability when integrated with various existing methods is proposed.
Su-Yuan Liu, Si-Wei Wang, Ke Liang et al.· IEEE Transactions on Pattern...· 1 citation
This work proposes SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation and establishes a scalable, agent-driven paradigm for computational biology.
Songhan Wang, Haoang Chi, He Li et al.· Proceedings of the 32nd ACM...· 1 citation
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