FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks
arxiv(2023)
摘要
Community search is a personalized community discovery problem designed to
identify densely connected subgraphs containing the query node. Recently,
community search in heterogeneous information networks (HINs) has received
considerable attention. Existing methods typically focus on modeling
relationships in HINs through predefined meta-paths or user-specified
relational constraints. However, metapath-based methods are primarily designed
to identify single-type communities with nodes of the same type rather than
multi-type communities involving nodes of different types. Constraint-based
methods require users to have a good understanding of community patterns to
define a suitable set of relational constraints, which increases the burden on
users. In this paper, we propose FCS-HGNN, a novel method for flexibly
identifying both single-type and multi-type communities in HINs. Specifically,
FCS-HGNN extracts complementary information from different views and
dynamically considers the contribution of each relation instead of treating
them equally, thereby capturing more fine-grained heterogeneous information.
Furthermore, to improve efficiency on large-scale graphs, we further propose
LS-FCS-HGNN, which incorporates i) the neighbor sampling strategy to improve
training efficiency, and ii) the depth-based heuristic search strategy to
improve query efficiency. We conducted extensive experiments to demonstrate the
superiority of our proposed methods over state-of-the-art methods, achieving
average improvements of 14.3
communities, respectively.
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