SimRank

About: SimRank is a research topic. Over the lifetime, 250 publications have been published within this topic receiving 21163 citations.

Improving Matching Process in Social Network

Abstract: Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.

Practical Algorithms and Lower Bounds for Similarity Search in Massive Graphs

Abstract: To exploit the similarity information hidden in the hyperlink structure of the Web, this paper introduces algorithms scalable to graphs with billions of vertices on a distributed architecture. The similarity of multistep neighborhoods of vertices are numerically evaluated by similarity functions including SimRank, a recursive refinement of cocitation, and PSimRank, a novel variant with better theoretical characteristics. Our methods are presented in a general framework of Monte Carlo similarity search algorithms that precompute an index database of random fingerprints, and at query time, similarities are estimated from the fingerprints. We justify our approximation method by asymptotic worst-case lower bounds: we show that there is a significant gap between exact and approximate approaches, and suggest that the exact computation, in general, is infeasible for large-scale inputs. We were the first to evaluate SimRank on real Web data. On the Stanford WebBase graph of 80M pages the quality of the methods increased significantly in each refinement step until step four

Accelerating pairwise SimRank estimation over static and dynamic graphs

Abstract: Measuring similarities among different vertices is a fundamental problem in graph analysis. Among different similarity measurements, SimRank is one of the most promising and popular. In reality, instead of computing the whole similarity matrix, people often issue SimRank queries in a pairwise manner, each of which needs to estimate an approximate SimRank value within a specified accuracy for a given pair of nodes. These pairwise SimRank queries are often processed on real-life graphs, which typically evolve over time, requiring efficient algorithms that can query pairwise SimRank under dynamic graph updates. However, current single-pair SimRank solutions are either static or inefficient in handling dynamic cases with good-quality results. Observing that the sample size is the major factor that determines the efficiency and the accuracy in Monte Carlo methods to estimate pairwise SimRank, in this paper, we propose three algorithms to query pairwise SimRank over static and dynamic graphs efficiently, by using different sample reduction strategies. The accuracy of our algorithms is guaranteed by the different invariants we propose for pairwise SimRank. We show that our algorithms outperform the state-of-the-art static and dynamic solutions for pairwise SimRank estimation.

Measuring Similarity Based on Link Information: A Comparative Study

Abstract: Measuring similarity between objects is a fundamental task in domains such as data mining, information retrieval, and so on. Link-based similarity measures have attracted the attention of many researchers and have been widely applied in recent years. However, most previous works mainly focus on introducing new link-based measures, and seldom provide theoretical as well as experimental comparisons with other measures. Thus, selecting the suitable measure in different situations and applications is difficult. In this paper, a comprehensive analysis and critical comparison of various link-based similarity measures and algorithms are presented. Their strengths and weaknesses are discussed. Their actual runtime performances are also compared via experiments on benchmark data sets. Some novel and useful guidelines for users to choose the appropriate link-based measure for their applications are discovered.

E-rank: A Structural-Based Similarity Measure in Social Networks

Abstract: With the social networks (SNs) becoming ubiquitous and massive, the issue of similarity computation among entities becomes more challenging and draws extensive interests from various research fields. SimRank is a well known similarity measure, however it considers only the meetings between two nodes that walk along equal length paths since the path length increases strictly with the iteration increasing during the similarity computation, besides, it does not differentiate importance for each link. In this paper, we propose a novel structural similarity measure, E-Rank (Entity Rank), towards effectively computing the structural similarity of entities in SNs, based on the intuition that two entities are similar if they can arrive at common entities. E-Rank can be well applied to social networks for measuring similarities of entities. Extensive experiments demonstrate the effectiveness of E-Rank by comparing with the state-of-the-art measures.