Abstract
Community search (CS) aims to find a cohesive community that satisfies query conditions in a given information network. Recent studies have introduced the CS problem into heterogeneous information networks (HINs) that are composed of multi-typed vertices and edges. However, existing works of community search in HINs ignore the influence of vertices and community. To solve this problem, we propose the concept of heterogeneous influence and a new model called heterogeneous k-influential community (\(k{\mathcal{P}}\)-HICs) which is designed by combining the concept of heterogeneous influence, meta-path, and k-core. Based on the model, we then develop three algorithms to find top-r \(k{\mathcal{P}}\)-HICs in the heterogeneous community containing the query vertex. The Basic-Peel and Advanced-Peel algorithms find top-r \(k{\mathcal{P}}\)-HICs by repeatedly peeling the low influential vertices. Considering the fact that top-r \(k{\mathcal{P}}\)-HICs are composed of vertices with high influence, the Reversed-Peel algorithm finds top-r \(k{\mathcal{P}}\)-HICs in a high influence vertices composed set and thus is more efficient. Extensive experiments have been performed on three real large HINs, and the results show that the proposed methods are effective for searching top-r \(k{\mathcal{P}}\)-HICs.
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Li, X., Zhou, L., Kong, B., Wang, L. (2023). Influential Community Search Over Large Heterogeneous Information Networks. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_12
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