论文标题
直接比较社交网络中的社区结构
Towards Direct Comparison of Community Structures in Social Networks
论文作者
论文摘要
通常，通过比较使用不同算法获得的社区的评估度量值来评估社区检测算法。用于衡量社区质量的评估指标包含了实体的拓扑信息，例如社区内部或外部节点的连通性。但是，在比较度量值的同时，它失去了社区拓扑信息的直接涉及比较过程。在本文中，提出了一种直接的比较方法，即直接比较了用两种算法获得的社区的拓扑信息。质量度量是基于社区拓扑信息的直接比较而设计的。考虑到新设计的质量度量，开发了两个排名方案。使用八种广泛使用的现实世界数据集和六种社区检测算法研究了建议的质量指标以及排名方案的功效。
Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely \emph{Topological Variance (TV)} is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms.
