Semi-supervised Community Detection Framework Based on Non-negative Factorization Using Individual Labels

Abstract

Community structure is one of the most significant properties of complex networks and is a foundational concept in exploring and analyzing networks. Researchers have concentrated partially on the topology information for community detection before, ignoring the prior information of the complex networks. However, background information can be obtained from the domain knowledge in many applications in advance. Especially, the labels of some nodes are already known, which indicates that a point exactly belongs to a specific category or does not belong to a certain one. Then, how to encode these individual labels into community detection becomes a challenging and interesting problem. In this paper, we present a semi-supervised framework based on non-negative matrix factorization, which can effectively incorporate the individual labels into the process of community detection. Promising experimental results on synthetic and real networks are provided to improve the accuracy of community detection.