Abstract
Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.
Highlights
In many different disciplines, relations exist in the form of networks, such as social networks [1,2,3] and biological networks [4]
We propose an efficient semi-supervised community detection framework based on Nonnegative Matrix Factorization (NMF), which combines the prior information with the topology to extract high-quality communities
The semi-supervised framework we develop is flexible, in which the two graph regularizations can be introduced to other basic community detection algorithms such as spectral clustering algorithm to guide the detection process with prior information
Summary
Relations exist in the form of networks, such as social networks [1,2,3] and biological networks [4]. Community detection is critical for better understanding the networks especially huge networks, which enables us to observe and analyze the networks from community level It promotes other related social computing tasks such as recommendation systems [6]. Several methods designed for unsigned networks have been proposed to consider the prior information [10,11,12,13,14,15,16,17], which are named as semi-supervised community detection. We propose an efficient semi-supervised community detection framework based on Nonnegative Matrix Factorization (NMF), which combines the prior information with the topology to extract high-quality communities. 2. The semi-supervised framework we develop is flexible, in which the two graph regularizations can be introduced to other basic community detection algorithms such as spectral clustering algorithm to guide the detection process with prior information.
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