Narrow Results

In this paper, we propose weighted symmetric binary matrix factorization (wSBMF) framework to detect overlapping communities in bipartite networks, which describes the relationships between two types of nodes. Our method improves performance by recognizing the distinction between two types of missing edges — ones among the nodes in each node type and the others between two node types. Our method can also explicitly assign community membership and distinguish outliers from overlapping nodes, as well as incorporating existing knowledge on the network. We propose a generalized partition density for bipartite networks as a quality function, which identifies the most appropriate number of communities. The experimental results on both synthetic and real-world networks demonstrate the effectiveness of our method.

The relationship between nodes in the network is reflected by edges. It is novel to study the network from the perspective of edges rather than nodes. Following the idea of edge graph clustering which provides an unorthodox approach to represent the topology of the systems, we propose a new clustering method called Spectral Analysis based on Weighted edge graphs to find the overlapping clusters. According to the incidence matrix of the original network, we obtain the corresponding edge graph. The first two nontrivial eigenvectors dimensional spaces are built with the Laplacian Matrix, and we get edge dendrogram of the network. Then cut the edge dendrogram by using the improved partition density to get the optimal community structure. The proposed algorithm successfully finds the common nodes between clusters. Experiments on five real-world networks show that the proposed SAWEG algorithm performs better than the other three benchmarking clustering algorithms.