Abstract
A heterogeneous correlation network represents relationships (edges) among source-typed and attribute-typed objects (nodes). It can be used to model an academic collaboration network, describing connections among authors and published papers. To date, there has been little research into mining communities in heterogeneous networks. The objective of our research is to discover overlapping communities that include all node and edge types in a heterogeneous correlation network. We describe an algorithm, OHC, that detects overlapping communities in heterogeneous correlation networks. Inspired by a homogeneous community scoring function, Triangle Participation Ratio (TPR), OHC finds target heterogeneous communities then expands them recursively with triangle-forming nodes. Experiments on different real world networks demonstrate that OHC identifies heterogeneous communities that are tightly connected internally according to two traditional scoring functions. Additionally, analyzing the top ranking heterogeneous communities in a case study, we evaluate the results qualitatively.
Funded by New Zealand Callaghan Innovation and Pingar, under an R&D Student Fellowship Grant (Contract Number: PTERN1502).
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Bian, R., Koh, Y.S., Dobbie, G., Divoli, A. (2018). OHC: Uncovering Overlapping Heterogeneous Communities. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_20
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DOI: https://doi.org/10.1007/978-3-030-03991-2_20
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