Index-based Structural Clustering on Directed Graphs
Lingkai Meng, Long Yuan, Zi Chen, Xuemin Lin, Shiyu Yang
Abstract
Structural clustering (SCAN) is one of the most popular graph clustering paradigms. However, SCAN assumes that the input graph is undirected and can not cluster the directed graphs. To address this problem, in this paper, we propose a new structural clustering model based on SCAN to cluster directed graphs. Following the new model, we propose an index-based approach to support the efficient clustering of a given graph. Moreover, we also devise efficient index maintenance algorithms to handle the case that the input graph is dynamically updated. We conduct extensive experiments on nine real directed graphs, one of which contains more than 2 billion edges. The results demonstrate the effectiveness and efficiency of our proposed methods.
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Cited by top-tier papers3
- Triangle Counting in Hypergraph Streams: A Complete and Practical ApproachLingkai Meng, Long Yuan, Xuemin Lin, Wenjie Zhang et al.SIGMOD 2026 · 4 citations
- Searching and Detecting Structurally Similar Communities in Large Heterogeneous Information NetworksShu Wang, Yixiang Fang, Wensheng LuoVLDB 2025 · 3 citations
- Efficient Structural Clustering Over HypergraphsDong Pan, Xu Zhou, Lingwei Li, Quanqing Xu et al.ICDE 2025
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