# MINC-NRL: An Information-Based Approach for Community Detection
###### Abstract  
Complex networks usually consist of dense-connected cliques, which are defined as communities. A community structure is a reflection of the local characteristics existing in the network topology, this makes community detection become an important research field to reveal the internal structural characteristics of networks. In this article, an information-based community detection approach MINC-NRL is proposed, which can be applied to both overlapping and non-overlapping community detection. MINC-NRL introduces network representation learning (NRL) to represent the target network as vectors, then generates a community evolution process based on these vectors to reduce the search space, and finally, finds the best community partition in this process using mutual information between network and communities (MINC). Experiments on real-world and synthetic data sets verifies the effectiveness of the approach in community detection, both on non-overlapping and overlapping tasks. community detection; mutual information; network representation learning +
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In recent years, with the development of representation learning algorithm in natural language processing (NLP) and other fields, researchers have begun to work on the relationship between network representation learning and community detection, such as using community annotations to improve network representation algorithms [5; 6] and using network representation algorithms to improve the accuracy of community detection [6]. After induction and summary of these studies, we found that a typical community detection method often consists of two parts: The first is the community evaluation index, which measures the strength and weakness of a community structure; the second is the community transformer, which constantly change the community structure and tries to find a partition that reach some peak values of the evaluation index, that is, the solutions of community detection. In the ideal case, the community evaluation index is able to evaluate the goodness of the community structure precisely, and the community transformer can efficiently search to give a set of partitions which cover the optimal partition. Unfortunately, for the former, since there is no accurate definition of the merits of community structure, researchers can only design community evaluation indices by means of edge density (e.g., modularity) or information-theoretic representation of node clusters (e.g., information entropy), so as to fit the real-world cases as well as possible. For the latter, community transformers have to make a trade-off between efficiency and the accuracy. Most of the methods have no way to use the global information of the network while transforming the community structure. For instance, while propagating labels in label propagation algorithm, to which a node propagates a label depends only on its local structural environment. Aiming at the problems above, a new approach called MINC-NRL is proposed. The main idea is by using network representation learning (NRL) algorithms such as the Deepwalk method [7], the network is represented as vectors, which reflects the global information of the network. A community evolution process is then generated based on these vectors using hierarchically clustering to reduce the search space. Finally, by the usage of mutual information between network and communities (MINC), the best state in this community evolution process is found, as the final result of community detection.