使用等级稳定性评估网络社区检测对可用元数据的适用性

Ryan Hartman, Josemar Faustino, Diego Pinheiro, R. Menezes
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引用次数: 9

摘要

在过去的二十年中,我们见证了数据结构分析的广泛应用。该领域通常被称为网络科学,专注于通过寻找从数据块之间的关系中出现的属性来理解复杂现象,而不是传统的对数据本身的挖掘。网络中常用的结构分析包括寻找子图内连接密度超过外部连接密度的子图;叫做社区检测。已经提出了许多技术来寻找社区,以及评估算法找到这些子结构的能力的基准。直到最近,文献大多忽略了这样一个事实,即这些社区往往代表了社区中元素的共同特征。例如,在社交网络中,社区可以代表:关注同一特定运动的人,来自同一教室的人,在同一研究领域工作的作者,等等。这里的问题是社区检测选择作为可用元数据提供的基础真相的函数之一。在这项工作中,我们建议从元数据的角度使用等级稳定性(等级熵)来评估使用不同技术识别的社区。我们使用跨多个社区检测技术的在线社会互动的大规模数据集来验证我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the suitability of network community detection to available meta-data using rank stability
In the last two decades, we have witnessed the widespread use of structural analysis of data. The area, generally called Network Science, concentrates on understanding complex phenomena by looking for properties that emerge from the relationships between the pieces of data instead of the traditional mining of the data itself. A commonly used structural analysis in networks consists of finding subgraphs whose density of connections within the subgraph surpasses that of outside connections; called Community Detection. Many techniques have been proposed to find communities as well as benchmarks to evaluate the algorithms ability to find these substructures. Until recently, the literature has mostly neglected the fact that these communities often represent common characteristic of the elements in the community. For instance, in a social network, communities could represent: people who follow the same particular sport, people from the same classroom, authors working in the same field of study, to name a few. The problem here is one of community detection selection as a function of the ground truth provided by available meta-data. In this work, we propose the use of rank stability (entropy of ranks) to assess communities identified using different techniques from the perspective of meta-data. We validate our approach using a large-scale data set of on-line social interactions across multiple community detection techniques.
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