On Multi-query Local Community Detection
| Autor: | Yuchen Bian, Wei Cheng, Xiang Zhang, Dongsheng Luo, Yaowei Yan, Wei Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Flexibility (engineering)
Computer science business.industry Community structure 02 engineering and technology Machine learning computer.software_genre Random walk Local community Set (abstract data type) Random walker algorithm 020204 information systems Node (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business computer |
| Zdroj: | ICDM |
| DOI: | 10.1109/icdm.2018.00016 |
| Popis: | Local community detection, which aims to find a target community containing a set of query nodes, has recently drawn intense research interest. The existing local community detection methods usually assume all query nodes are from the same community and only find a single target community. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, we may not have any prior knowledge about the community memberships of the query nodes, and different query nodes may be from different communities. To address this limitation of the existing methods, we propose a novel memory-based random walk method, MRW, that can simultaneously identify multiple target local communities to which the query nodes belong. In MRW, each query node is associated with a random walker. Different from commonly used memoryless random walk models, MRW records the entire visiting history of each walker. The visiting histories of walkers can help unravel whether they are from the same community or not. Intuitively, walkers with similar visiting histories are more likely to be in the same community. Moreover, MRW allows walkers with similar visiting histories to reinforce each other so that they can better capture the community structure instead of being biased to the query nodes. We provide rigorous theoretical foundation for the proposed method and develop efficient algorithms to identify multiple target local communities simultaneously. Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method. |
| Databáze: | OpenAIRE |
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