1 citations · 1 across the 17 of their papers we have counts for
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TCGU: Data-centric Graph Unlearning based on Transferable Condensation
Fan Li, Xiaoyang Wang, Dawei Cheng +3
With growing demands for data privacy and model robustness, graph unlearning (GU), which erases the influence of specific data on trained GNN models, has gained significant attenti…
Efficient Influence Minimization via Node Blocking
Jinghao Wang, Yanping Wu, Xiaoyang Wang +4
Given a graph G, a budget k and a misinformation seed set S, Influence Minimization (IMIN) via node blocking aims to find a set of k nodes to be blocked such that the expected spre…
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Fan Li, Xiaoyang Wang, Dawei Cheng +3
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly…
Deep Overlapping Community Search via Subspace Embedding
Qing Sima, Jianke Yu, Xiaoyang Wang +3
Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categorie…
Efficient Unsupervised Community Search with Pre-trained Graph Transformer
Jianwei Wang, Kai Wang, Xuemin Lin +2
Community search has aroused widespread interest in the past decades. Among existing solutions, the learning-based models exhibit outstanding performance in terms of accuracy by le…
Neural Attributed Community Search at Billion Scale
Jianwei Wang, Kai Wang, Xuemin Lin +2
Community search has been extensively studied in the past decades. In recent years, there is a growing interest in attributed community search that aims to identify a community bas…