基于多标签传播的重叠社区发现优化算法

标签传播算法是一种被广泛应用的社区发现算法，该算法为网络中的每个节点分配一个初始标签，然后通过传播标签来发现复杂网络中的潜在社区，具有时间复杂度低的特点。当前基于标签传播的重叠社区发现算法存在忽略节点重要性差异、需要人为设置参数等不足。针对该类算法在重叠社区发现方面的缺陷，提出一种基于多标签传播的重叠社区发现优化算法。该算法使用K-核分解方法找出若干个社区核心节点，以这些节点为种子节点，逐层向外传播标签；在进行标签选择的时候以邻居节点标签的种类来决定重叠节点的标签个数。实验表明，该算法明显改善了社区发现的性能，提高了划分结果的稳定性和准确性。

Overlapping Community Detection Algorithm Based on Improved Multi-label Propagation

Label propagation is a widely used community detection method with low complexity. It assigns an initial label for each node in the network, and then propagates the labels to discover the potential community structure in complex networks. However traditional label propagation is faced with some inadequacies, such as ignoring the difference between nodes and input parameters demanding. To overcome those defects, this paper puts forward an overlapping community detection algorithm based on the improved multi-label propagation. It uses K-shell decomposition method to identify core nodes of the network firstly, and then updates labels outward layer by layer. The number of labels of overlapping nodes is determined by the types of neighbor node when choosing label for a node. Experiment results show that this algorithm makes the community detection results more accurate and stable.