A Framework for Community Detection in Heterogeneous Multi-Relational Networks

Abstract

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting preplanted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.

Multidimensional community discovering in heterogeneous social networks

“…Recently, several researches have addressed community detection in heterogeneous multirelational networks. We can divide the related work into five categories based on the techniques used on the multidimensional community detection process, which are modularity optimization, 27 tensor factorization, 28 matrix factorization, 29 stochastic models based approach, 30 and label propagation based approach 31 …”

“…We have compute the composite modularity 27 to evaluate the partition of the related node sets defined as follows: where G [ y ] denotes the subnetwork which consists of the set of hyperedges E [ y ] and the incident nodes, and . Here m [ y ] =| E [ y ] | is the number of edges in G [ y ] , m is the total number of edges, and Q [ y ] is the modularity in G [ y ] .…”

“…Authors in Xin et al 27 proposed a new approach, called Louvain‐C, for detecting communities in a heterogeneous multirelational network, which followed the line of the modularity optimization method.…”

Community Detection of Heterogeneous Networks with RankClus Based on Spark GraphX

“…Because of the characteristics of multi-dimensional and multimode in the heterogenous, some researchers use data reconstruction method [9][10] and dimensionality reduction method [11][12] [13] to convert it into relatively simple network type or reduce the dimension. Liu et al [9] transform an original heterogeneous network into a bipartite network and perform community detection on it.…”

“…Liu et al [9] transform an original heterogeneous network into a bipartite network and perform community detection on it. Each node and edge or hyperedge in the original heterogeneous network is, respectively, mapped into a vertex node and a link node in the bipartite network.…”