Community search (CS) is a fundamental problem in graph analysis, which aims to identify a query-dependent cohesive subgraph that satisfies a specific community model and contains the given query vertices. While numerous CS approaches have been proposed, including non-learning-based and learning-based ones, existing re...
Yu-Han Zhou, Zheng Wu, Qing Liu et al.· Proceedings of the ACM on Ma...· 0 citations
An adaptive community search framework ECHO is proposed, which consistently outperforms state-of-the-art methods in terms of community quality while achieving superior search efficiency.
Chengyang Luo, Zi-Xing Ding, Qing Liu et al.· Proceedings of the 32nd ACM...· 0 citations
Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we i...
Xueqin Chang, Rui-Ze Liu, Qing Liu et al.· Proceedings of the 32nd ACM...· 0 citations
In this paper, for the first time, we study the community search problem over multimodal graphs. This task aims to identify a query vertex-containing subgraph that is both structurally cohesive and semantically coherent with multimodal query inputs (e.g., text and images). Existing community search methods fail to capt...
Chengyang Luo, Zi-Xing Ding, Qing Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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