Bipartite Link Prediction by Intra-Class Connection Based Triadic Closure

Jungwoon Shin, Keonwoo Kim, Donghyeon Park, Sunkyu Kim, Jaewoo Kang

    Research output: Contribution to journalArticlepeer-review

    Abstract

    A variety of real-world systems can be formulated as bipartite link prediction problems where two different types of nodes exist and no links connect nodes of the same type. In link prediction, triadic closure is an important property that describes how new links are formed. However, triadic closure is difficult to apply to bipartite link prediction tasks because the triadic closure property, which states that new edges tend to form triangles, does not hold true in bipartite settings. In this paper, we introduce Intra-class Connection based Triadic Closure (ICTC) which is a method that can use triadic closure even when the nodes in the same set are unconnected. ICTC aggregates the link probabilities of many local triads, which are edges between triples of nodes, to predict the probability of a link existing between nodes. Specifically, the probability of an edge in a triangle is calculated by multiplying the probabilities of two other edges. The experimental results on eight real-world datasets show that our method outperforms state-of-the-art methods in most cases.

    Original languageEnglish
    Article number9144230
    Pages (from-to)140194-140204
    Number of pages11
    JournalIEEE Access
    Volume8
    DOIs
    Publication statusPublished - 2020

    Keywords

    • Link prediction
    • bipartite networks
    • intra-class connection
    • triadic closure

    ASJC Scopus subject areas

    • Computer Science(all)
    • Materials Science(all)
    • Engineering(all)

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