标题： 归一化互信息用于评估重叠社区发现算法 显示英文标题

标题： Normalized Mutual Information to evaluate overlapping community finding algorithms

摘要： 随着重叠聚类算法的日益流行，尤其是在社会网络分析中，需要定量度量来衡量一种方法的准确性。 给定一组真实的聚类，以及算法找到的聚类集合，这些聚类集合必须进行比较，以查看它们之间的相似性或差异性。 在许多情况下，希望使用归一化度量，例如当两个集合完全不相似时赋予0值，当它们相同时赋予1值。 基于归一化互信息的度量[1]最近变得流行起来。 我们展示了该度量的反直觉行为，并表明通过使用更传统的归一化方法可以对其进行修正。 我们将结果与其他度量的结果进行比较，例如Omega指数[2]。 显示英文摘要

摘要： Given the increasing popularity of algorithms for overlapping clustering, in particular in social network analysis, quantitative measures are needed to measure the accuracy of a method. Given a set of true clusters, and the set of clusters found by an algorithm, these sets of clusters must be compared to see how similar or different the sets are. A normalized measure is desirable in many contexts, for example assigning a value of 0 where the two sets are totally dissimilar, and 1 where they are identical. A measure based on normalized mutual information, [1], has recently become popular. We demonstrate unintuitive behaviour of this measure, and show how this can be corrected by using a more conventional normalization. We compare the results to that of other measures, such as the Omega index [2].