Lizhong Yao
ABSTRACT
In this paper, we present a novel fuzzy framework, dubbed as Fuzzy Multi-View Featured Network Clustering (FMVFNC), for effectively uncovering overlapping communities in social network data. Unlike most previous efforts which utilize only edge structure and single view of vertex features to perform the community discovery task, the proposed FMVFNC is able to take advantage of both edge structure and correlated vertex features which may be collected from multiple views. As the uncovered social communities are described by both network structure and semantically correlated features from diverse modalities, their practical significance can be well revealed. We innovatively design a unified fuzzy objective for FMVFNC to perform the task. We then derive an iterative algorithm for the proposed framework to optimize the formulated objective function. FMVFNC has been tested with a number of well-established datasets and has been compared with a number of state-of-the-art baselines for community detection. The notable results obtained may validate the effectiveness of FMVFNC.
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