MatchSim: a novel similarity measure based on maximum neighborhood matching

Abstract

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.

Localization of vectors–patterns in the problems of parametric classification with the purpose of increasing its accuracy

“…A characteristic feature of the approach is application of the principle of evaluation by using preliminarily prepared databases of patterns for classes and input patterns. The rule of decision making is based on the criterion of similarity of Dice, applied usually for classification of input patterns, represented by the two-dimensional spectrum of the video image [19]. Similarity coefficients in the form (9) are calculated with the use of eigenvectors, obtained after orthogonal conversion of the two-dimensional spectrum.…”

C-Rank: A link-based similarity measure for scientific literature databases

“…Recently, various approaches have sought to improve the accuracy of existing measures [21]. MatchSim is one of the newest approaches.…”

“…In this section, we compare the accuracy of similarities computed by C-Rank to MatchSim [21]. C of C-Rank is 0.8.…”

“…Various similarity measures and their applications have been proposed in the past [3,4,10,13,20,21,26,38,46,48]. Text-based similarity measures count the number of keywords in common between two papers.…”

Geographic knowledge extraction and semantic similarity in OpenStreetMap

“…These approaches to similarity are based on some form of structural distance between nodes (e.g. edge counting), sometimes adding additional parameters to weight the paths [48], or on the topological comparison of subgraphs [35].…”