论文标题
二项式尾巴用于社区分析
Binomial Tails for Community Analysis
论文作者
论文摘要
网络中社区发现的一项重要任务是评估产生的候选人群体的结果和稳健排名的重要性。通常，在实践中发现了许多候选社区，并将分析师的时间集中在最突出和最有前途的发现上至关重要。我们开发了使用二项式模型从尾巴概率得出的简单有效的组评分功能。关于合成和众多现实世界数据的实验提供了证据，表明二项式评分会导致比其他廉价评分功能（例如电导率）更强大的排名。此外，我们获得可用于过滤和标记发现组的置信值（$ p $ - 值）。我们的分析阐明了该方法的各种特性。二项式尾巴简单且通用，我们描述了社区分析的另外两个应用：社区成员的程度（反过来产生群体得分功能），并在社区引起的图中发现了重要边缘。
An important task of community discovery in networks is assessing significance of the results and robust ranking of the generated candidate groups. Often in practice, numerous candidate communities are discovered, and focusing the analyst's time on the most salient and promising findings is crucial. We develop simple efficient group scoring functions derived from tail probabilities using binomial models. Experiments on synthetic and numerous real-world data provides evidence that binomial scoring leads to a more robust ranking than other inexpensive scoring functions, such as conductance. Furthermore, we obtain confidence values ($p$-values) that can be used for filtering and labeling the discovered groups. Our analyses shed light on various properties of the approach. The binomial tail is simple and versatile, and we describe two other applications for community analysis: degree of community membership (which in turn yields group-scoring functions), and the discovery of significant edges in the community-induced graph.
