SimRank

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

Sig-SR: SimRank search over singular graphs

Abstract: SimRank is an attractive structural-context measure of similarity between two objects in a graph. It recursively follows the intuition that "two objects are similar if they are referenced by similar objects". The best known matrix-based method [1] for calculating SimRank, however, implies an assumption that the graph is non-singular, its adjacency matrix is invertible. In reality, non-singular graphs are very rare; such an assumption in [1] is too restrictive in practice. In this paper, we provide a treatment of [1], by supporting similarity assessment on non-invertible adjacency matrices. Assume that a singular graph G has n nodes, with r(2+Kr2)) time for K iterations. In contrast, the only known matrix-based algorithm that supports singular graphs [1] needs O(r4n2) time. The experimental results on real and synthetic datasets demonstrate the superiority of InvSR on singular graphs against its baselines.

Efficient link-based similarity search in web networks

Abstract: The pre-computation cost in the off-line stage is significantly reduced.The efficiency of query processing is optimized by proposing a pruning algorithm.The accuracy loss of pruning algorithm is controlled by tuning threshold.The effectiveness of returned result is effective and acceptable. Similarity search in web networks, aiming to find entities similar to the given entity, is one of the core tasks in network analysis. With the proliferation of web applications, including web search and recommendation system, SimRank has been a well-known measure for evaluating entity similarity in a network. However, the existing work computes SimRank iteratively over a huge similarity matrix, which is expensive in terms of time and space cost and cannot efficiently support similarity search over large networks. In this paper, we propose a link-based similarity search method, WebSim, towards efficiently finding similar entities in web networks. WebSim defines the similarity between entities as the 2-hop similarity of SimRank. To reduce computation cost, we divide the similarity search process into two stages: off-line stage and on-line stage. In the off-line stage, the 1-hop similarities are computed, and an optimized algorithm is designed to reduce the unnecessary accumulation operations on zero similarities. In the on-line stage, the 2-hop similarities are computed, and a pruning algorithm is developed to support fast query processing through searching similar entries from a partial sums index derived from the 1-hop similarities. The index items that are lower than a given threshold are skipped to reduce the searching space. Compared to the iterative SimRank computation, the time and space cost of similarity computation is significantly reduced, since WebSim maintains only the similarity matrix of 1-hop that is much smaller than that of multi-hop. Experiments through comparison with SimRank and its optimized algorithms demonstrate that WebSim has on average a 99.83% reduction in the time cost and a 92.12% reduction in the space cost of similarity computation, and achieves on average 99.98% NDCG.

Parallel recommend method based on social network structure

Abstract: A parallel recommend method based on a social network structure comprises the following steps: 1, selecting k active nodes as central nodes according to an active node selection strategy; 2, using k central nodes as the initial cluster center, and dividing all nodes as k communities taking the k central nodes as centers; 3, using a SimRank algorithm to respectively calculate similarity between users of each community according to the k communities obtained in the previous step; 4, calculating a user list most similar to certain user; 5, repeating step 4 so as to obtain similar user lists of random users; 6, aiming at a target user, and analyzing user interest hobby in the similar user list so as to do customized recommend for the target user The parallel recommend method based on the social network structure is good in validity, and has good processing efficiency under a large data set

Efficient top-K SimRank-based similarity join

Abstract: SimRank is an effective and widely adopted measure to quantify the structural similarity between pairs of nodes in a graph In this paper we study the problem of top-k SimRank-based similarity join, which finds k pairs of nodes with the largest SimRank values To the best of our knowledge, this is the first attempt to address this problem We propose a random-walk-based method to efficiently identify top-k pairs Experiment results on real datasets show that our method significantly outperforms baseline approaches

Boosting SimRank with Semantics.

Abstract: The problem of estimating the similarity of a pair of nodes in an information network draws extensive interest in numerous fields, e.g., social networks and recommender systems. In this work we revisit SimRank, a popular andwell studied similarity measure for information networks, that quantifies the similarity of two nodes based on the similarity of their neighbors. SimRank’s popularity stems from its simple, declarative definition and its efficient, scalable computation. However, despite its wide adaptation, it has been observed that for many applications SimRank may yield inaccurate similarity estimations, due to the fact that it focuses on the network structure and ignores the semantics conveyed in the node/edge labels. Therefore, the question that we ask is can SimRank be enriched with semantics while preserving its