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RISS 인기검색어
- 검색키워드
- 주제어 : Link-based similarity measure (검색결과 2 건)
- 무료
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C-Rank: A link-based similarity measure for scientific literature databases
Yoon, S.H.,Kim, S.W.,Park, S. Elsevier science 2016 Information sciences Vol.326 No.-
<P>As the number of people who use scientific literature databases has grown, the demand for literature retrieval services has steadily increased. One of the most popular retrieval service methods is to find a set of papers similar to the paper under consideration, which requires a measure that computes the similarities between the papers. Scientific literature databases exhibit two interesting characteristics that are not found in general databases. First, the papers cited by older papers are often not included in the database due to technical and economic reasons. Second, since a paper references previously published papers, few papers cite recently published papers. These two characteristics cause all existing similarity measures to fail in at least one of the following cases: (1) measuring the similarity between old, but similar papers, (2) measuring the similarity between recent, but similar papers, and (3) measuring the similarity between two similar papers: one old, the other recent. In this paper, we propose a new link-based similarity measure called C-Rank, which uses both in-link and out-link references, disregarding the direction of the references. In addition, we discuss the most suitable normalization method for scientific literature databases and we propose an evaluation method for measuring the accuracy of similarity measures. For the experiments, we used real-world papers from DBLP's database with reference information crawled from Libra. We then compared the performance of C-Rank with that of existing similarity measures. Experimental results showed that C-Rank achieved a higher accuracy than existing similarity measures. (C) 2015 Elsevier Inc. All rights reserved.</P>
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JacSim: An accurate and efficient link-based similarity measure in graphs
Hamedani, M.R.,Kim, S.W. Elsevier science 2017 Information sciences Vol.414 No.-
<P>SimRank is a well-known link-based similarity measure successfully applied in many graph-related applications. Despite of the current success of SimRank, it suffers from the problem caused by its pairwise normalization paradigm in similarity computation. In this paper, we propose JacSim (Jaccard-based SimRank) that solves the pairwise normalization problem in an effective way. JacSim computes the similarity score of a node-pair by combining two different computation manners: Jaccard coefficient and pairwise normalization. We point out two problems of existing measures targeted at solving the pairwise normalization problem and provide effective solutions to them: (1) JacSim eliminates the redundancy hidden in their similarity computation; (2) JacSim enables to control the degree of importance of the two scores obtained by employing Jaccard coefficient and pairwise normalization. In order to take advantage of links weights in similarity computation, we propose a weighted version of JacSim applicable to weighted graphs. Furthermore, to accelerate JacSim, we provide a linear recursive matrix form of JacSim, which is composed of only linear operations. We demonstrate the effectiveness and efficiency of our JacSim by conducting extensive experiments with real-world datasets. The results show that JacSim outperforms existing measures significantly in term of accuracy and also provides better performance than the similarity measures targeted to solve the pairwise normalization problem. (C) 2017 Elsevier Inc. All rights reserved.</P>
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