论文标题
DCC：一种基于级联的方法来检测社交网络中的社区
DCC: A Cascade based Approach to Detect Communities in Social Networks
论文作者
论文摘要
社交网络中的社区检测与查找和分组网络固有的最相似节点有关。这些类似的节点是通过计算TIE强度来识别的。较强的联系表明连接的节点对共享较高的接近度。这项工作是由格拉纳诺维特（Granovetter）的论点激发的，该论点表明，牢固的联系在密度连接的节点内，以及现实世界网络中社区核心的理论密切相关。在本文中，我们引入了一种新的方法，称为\ emph {使用Cascades（DCC）}}}}，该方法证明了新的基于局部密度的TIE强度测量对检测社区的有效性。在这里，领带强度可用于决定传播信息的遵循的路径。这个想法是通过提高领带力量来指导社区核心的元组信息。考虑到级联生成步骤，已经开发了一种新颖的优惠构件方法，以将社区标签分配给未分配的节点。 $ DCC $的功效已根据几个现实世界数据集和基线社区检测算法的质量和准确性进行了分析。
Community detection in Social Networks is associated with finding and grouping the most similar nodes inherent in the network. These similar nodes are identified by computing tie strength. Stronger ties indicates higher proximity shared by connected node pairs. This work is motivated by Granovetter's argument that suggests that strong ties lies within densely connected nodes and the theory that community cores in real-world networks are densely connected. In this paper, we have introduced a novel method called \emph{Disjoint Community detection using Cascades (DCC)} which demonstrates the effectiveness of a new local density based tie strength measure on detecting communities. Here, tie strength is utilized to decide the paths followed for propagating information. The idea is to crawl through the tuple information of cascades towards the community core guided by increasing tie strength. Considering the cascade generation step, a novel preferential membership method has been developed to assign community labels to unassigned nodes. The efficacy of $DCC$ has been analyzed based on quality and accuracy on several real-world datasets and baseline community detection algorithms.
