Fuzzy Clustering in a Complex Network Based on Content Relevance and Link Structures

Many real-world problems can be represented as complex networks with nodes representing different objects and links between nodes representing relationships between objects. As different attributes can be considered as associating with different objects, other than nontrivial link structures, complex networks also contain rich content information, and it can be a big challenge to find interesting clusters in such networks by fully exploiting the knowledge of both content and link information in them. Although some attempts have been made to tackle this clustering problem, few of them have considered the feasibility of identifying clusters in complex networks using a fuzzy-based clustering approach. We believe that, if the degree of membership to a cluster that a node belongs to can be considered, we will be able to better identify clusters in complex networks, as we may be able to identify overlapping clusters. In this paper, we, therefore, propose a fuzzy-based clustering algorithm for this task. The algorithm, which we call Fuzzy Clustering Algorithm for Complex Networks (FCAN), can discover clusters by taking into consideration both link and content information. It does so by first processing the content information by introducing a measure to quantify the relevance of contents between each pair of nodes within the network. It then proceeds to leverage the link information in the clustering process by considering a measure of cluster density. Based on these measures, FCAN identifies fuzzy clusters that are more densely connected and more highly relevant in their contents to optimize the degrees of memberships of each node belonging to different clusters. The performance of FCAN has been evaluated with several synthetic and real datasets involving those of document classification and social community detection. The results show that, in terms of accuracy, computation efficiency, and scalability, FCAN can be a very promising approach.

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