Detecting dynamic community by fusing network embedding and nonnegative matrix factorization

Abstract

Many systems in nature and society, ranging from types of cancers to engineering, can be modeled as temporal networks. Identification of dynamic communities is fundamental for investigating the mechanisms of systems. Compared with community detection in static networks, dynamic community detection is challenging because it simultaneously leverages clustering accuracy with clustering drift. Current methods employ the temporal smoothness framework to balance these two items, but the inaccuracy and complexity of these methods have been criticized. To overcome these issues, a novel algorithm called NE2NMF for dynamic community detection is proposed by fusing Network Embedding based Evolutionary Nonnegative Matrix Factorization. Specifically, we use third-order smoothness to characterize clustering drift by fusing snapshots of previous, current, and subsequent time steps, thereby improving the accuracy of the characterization of dynamic communities. Moreover, to reduce time complexity, we decompose the network embedding matrix, rather than the adjacency matrix of each snapshot on the basis of the equivalence between evolutionary non-negative matrix factorization (ENMF) and network embedding. Results demonstrate that NE2NMF outperforms state-of-the-art approaches in terms of accuracy and robustness.