论文标题
关于使用局部结构特性来提高层次社区检测方法的效率
On the use of local structural properties for improving the efficiency of hierarchical community detection methods
论文作者
论文摘要
社区检测是对复杂网络分析的基本问题。它是网络数据挖掘中聚类的类似物。在社区检测方法中，分层算法很受欢迎。但是，它们的迭代性质以及需要重新计算用于分裂网络的结构属性（即Girvan和Newman's算法中的边缘之间的结构属性），使它们不适合大型网络数据集。在本文中，我们研究了如何将局部结构网络属性用作提高层次社区检测效率的代理，同时在模块化方面取得了竞争成果。特别是，我们研究了通常用于执行本地链接预测的结构属性的潜在用途，这是一个与社区结构相关的监督学习问题，因为节点很容易与社区内的其他节点建立新的联系。此外，我们检查网络修剪启发式方法的性能影响是一种辅助策略，以使等级社区检测更有效
Community detection is a fundamental problem in the analysis of complex networks. It is the analogue of clustering in network data mining. Within community detection methods, hierarchical algorithms are popular. However, their iterative nature and the need to recompute the structural properties used to split the network (i.e. edge betweenness in Girvan and Newman's algorithm), make them unsuitable for large network data sets. In this paper, we study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection while, at the same time, achieving competitive results in terms of modularity. In particular, we study the potential use of the structural properties commonly used to perform local link prediction, a supervised learning problem where community structure is relevant, as nodes are prone to establish new links with other nodes within their communities. In addition, we check the performance impact of network pruning heuristics as an ancillary tactic to make hierarchical community detection more efficient
