Motif-based embedding for graph clustering

Community detection in complex networks is a fundamental problem that has been extensively studied owing to its wide range of applications. However, because community detection methods typically rely on the relations between vertices in networks, they may fail to discover higherorder graph substructures, called the network motifs. In this paper, we propose a novel embedding method for graph clustering that considers higher-order relationships involving multiple vertices. We show that our embedding method, which we call motif-based embedding, is more e. ective in detecting communities than existing graph embedding methods, spectral embedding and force-directed embedding, both theoretically and experimentally.
Publisher
IOP PUBLISHING LTD
Issue Date
2016-12
Language
English
Keywords

COMMUNITY DETECTION; COMPLEX NETWORKS; MODELS

Citation

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT

ISSN
1742-5468
DOI
10.1088/1742-5468/2016/12/123401
URI
http://hdl.handle.net/10203/218221
Appears in Collection
KSE-Journal Papers(저널논문)
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