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MatchSim: a novel similarity measure based on maximum neighborhood matching

机译：MatchSim：一种基于最大邻域匹配的新颖相似性度量

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摘要

Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that "similar objects have similar neighbors" and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy.
机译：在图形中测量对象相似性是各种应用程序领域中的基本数据挖掘问题，包括Web链接挖掘，社交网络分析，信息检索和推荐系统。在本文中，我们基于基于“相似对象具有相似邻居”的直觉的基于邻居的方法，并提出了一种称为MatchSim的新颖相似性度量。我们的方法通过两个对象之间最大匹配的相似邻居对的平均相似性来递归定义两个对象之间的相似性。我们证明MatchSim符合相似性的基本直觉。因此，它可以克服SimRank中违反直觉的矛盾。此外，通过考虑邻居之间的相似性，MatchSim可以看作是传统邻居计数方案的扩展，从而具有更高的灵活性。我们提出MatchSim分数计算过程，并证明其收敛性。我们还分析了其时间和空间复杂度，并提出了两种加速技术：（1）提出一种简单的修剪策略，（2）采用近似算法进行最大匹配计算。实际数据集上的实验结果表明，尽管我们的方法在计算上效率较低，但在准确性方面却优于传统方法。

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