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26 May 2023 Weighted k-core community search on heterogeneous information networks
Dan Liu, Wei-Qiang Peng
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Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 1270006 (2023) https://doi.org/10.1117/12.2682275
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China
Abstract
Community search is a widely used technique in graph data mining that aims to find communities containing a given query node. While existing works have mainly focused on homogeneous information networks, most real-world networks are heterogeneous. To address this, this paper proposes a weighted k-core community search method designed for heterogeneous information networks. Firstly, the influence of the association weight between nodes based on meta-paths on the community search results is considered, and a weighted k-core community model (k, P)-Wcore is established, thereby improving the accuracy of community search. Subsequently, in order to improve search efficiency, an optimization algorithm OptWcore based on graph traversal search space is designed. This algorithm can effectively reduce redundant calculations and reduce the depth of path search, thereby improving search efficiency. Finally, experiments conducted on four real-world heterogeneous information network datasets demonstrate the effectiveness and efficiency of the proposed method.
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Dan Liu and Wei-Qiang Peng "Weighted k-core community search on heterogeneous information networks", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 1270006 (26 May 2023); https://doi.org/10.1117/12.2682275
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KEYWORDS
Detection and tracking algorithms
Algorithm development
Evolutionary algorithms
Mathematical optimization
Semantics
Social networks
Data mining
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