关键词: 复杂网络, 重叠社区, K-核迭代因子, 标签传播, 特征向量中心性, 标签熵

Abstract: Overlapping community structure mining aims to discover overlapping parts between multiple independent communities in a complex network, and it has a wide range of applications in social, transportation, public opinion, and even anti-terrorism fields. However, the current overlapping community mining algorithm based on label propagation shows strong randomness in networks with fuzzy community structure, resulting in low accuracy. Aiming at the problem of uncertainty and low accuracy caused by fuzzy boundaries of overlapping communities, a label propagation algorithm combining eigenvector centrality and label entropy (ECLE-LPA) is proposed. ECLE-LPA utilizes the K-nuclear iteration factor and the eigenvector centrality of the node to calculate the node influence and initialize the node label. In the propagation process, the label entropy and the closeness of the nodes are calculated to update the label list and the label membership, so as to overcome the recognition problem of fuzzy boundaries of overlapping communities. The experimental results show that: in real networks such as Les, Polbooks, Football, Polblogs, Netscience, etc., the EQ value of ECLE-LPA algorithm is generally increased by 1%～3% compared with the contrast algorithm. In the artificial network, the NMI value of ECLE-LPA is more than 10% higher than that of the contrast algorithm.

Key words: complex network, overlapping community, K-nuclear iteration factor, label propagation, eigenvector centrality, label entropy