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Higher-order Spectral Clustering for Heterogeneous Graphs. (arXiv:1810.02959v1 [cs.SI])
来源于:arXiv
Higher-order connectivity patterns such as small induced sub-graphs called
graphlets (network motifs) are vital to understand the important components
(modules/functional units) governing the configuration and behavior of complex
networks. Existing work in higher-order clustering has focused on simple
homogeneous graphs with a single node/edge type. However, heterogeneous graphs
consisting of nodes and edges of different types are seemingly ubiquitous in
the real-world. In this work, we introduce the notion of typed-graphlet that
explicitly captures the rich (typed) connectivity patterns in heterogeneous
networks. Using typed-graphlets as a basis, we develop a general principled
framework for higher-order clustering in heterogeneous networks. The framework
provides mathematical guarantees on the optimality of the higher-order
clustering obtained. The experiments demonstrate the effectiveness of the
framework quantitatively for three important applications including (i)
clustering, (ii) 查看全文>>