基于LeaderRank和节点相似性的多标签传播重叠社团挖掘算法

针对基于多标签传播重叠社团挖掘算法COPRA因随机更新策略带来的不稳定性以及需要预先输入参数的局限性等问题，提出一种基于LeaderRank和节点相似性的多标签传播重叠社团挖掘算法.该算法首先利用LeaderRank算法对网络中的节点进行重要性排序从而确定节点的更新顺序，减少标签不必要的更新.在标签传播过程中，根据节点相似性重新设计标签的更新策略，提高算法的稳定性.将算法应用于人工网络和真实网络中进行实验，实验结果表明该算法在挖掘重叠社团上具有较高的准确性和稳定性.

Multi-Label Propagation Algorithm for Overlapping Community Detection Based on LeaderRank and Node Similarity

The defects of overlapping community detection algorithm COPRA based on multi-label propagation include instability and pre-parameter limits, this study proposed a multi-label propagation algorithm for overlapping community detection based on LeaderRank and the node similarity. The algorithm uses the LeaderRank algorithm to sort the nodes in the network to determine the order of nodes updating. Then, re-design the label update strategy according to the similarity of nodes to improve the stability of the algorithm. The algorithm is applied to the artificial network and the real networks. The experimental results show that the proposed algorithm has high accuracy and stability for detecting overlapping communities.