School of Software, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2019 Jan 10;19(2):260. doi: 10.3390/s19020260.
With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.
随着实体信息在现实世界中的丰富，提出并广泛研究了许多带有属性节点的网络。在这些有属性的网络中进行社区检测是一项基本任务，旨在找到节点内部连接比节点之间连接更紧密的群组。然而，许多现有的有属性网络中的社区检测方法在设计算法时并没有区分重叠社区和非重叠社区。在本文中，我们提出了一种新颖而准确的算法，称为基于节点相似性的多标签传播算法（NMLPA），用于检测有属性网络中的重叠社区。NMLPA 首先计算节点之间的相似度，然后根据网络结构和节点相似度传播多个标签。此外，NMLPA 使用剪枝策略将每个节点的标签数量保持在适当的范围内。在合成和真实网络上进行的广泛实验表明，我们的新方法明显优于最先进的方法。
