SimRank

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

Efficient Algorithm for Computing Link-Based Similarity in Real World Networks

Abstract: Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.

Delta-SimRank computing on MapReduce

Abstract: Based on the intuition that "two objects are similar if they are related to similar objects", SimRank (proposed by Jeh and Widom in 2002) has become a famous measure to compare the similarity between two nodes using network structure. Although SimRank is applicable to a wide range of areas such as social networks, citation networks, link prediction, etc., it suffers from heavy computational complexity and space requirements. Most existing efforts to accelerate SimRank computation work only for static graphs and on single machines. This paper considers the problem of computing SimRank efficiently in a distributed system while handling dynamic networks which grow with time. We first consider an abstract model called Harmonic Field on Node-pair Graph. We use this model to derive SimRank and the proposed Delta-SimRank, which is demonstrated to fit the nature of distributed computing and can be efficiently implemented using Google's MapReduce paradigm. Delta-SimRank can effectively reduce the computational cost and can also benefit the applications with non-static network structures. Our experimental results on four real world networks show that Delta-SimRank is much more efficient than the distributed SimRank algorithm, and leads to up to 30 times speed-up in the best case1.

An experimental evaluation of simrank-based similarity search algorithms

Abstract: Given a graph, SimRank is one of the most popular measures of the similarity between two vertices. We focus on efficiently calculating SimRank, which has been studied intensively over the last decade. This has led to many algorithms that efficiently calculate or approximate SimRank being proposed by researchers. Despite these abundant research efforts, there is no systematic comparison of these algorithms. In this paper, we conduct a study to compare these algorithms to understand their pros and cons.We first introduce a taxonomy for different algorithms that calculate SimRank and classify each algorithm into one of the following three classes, namely, iterative-, non-iterative-, and random walk-based method. We implement ten algorithms published from 2002 to 2015, and compare them using synthetic and real-world graphs. To ensure the fairness of our study, our implementations use the same data structure and execution framework, and we try our best to optimize each of these algorithms. Our study reveals that none of these algorithms dominates the others: algorithms based on iterative method often have higher accuracy while algorithms based on random walk can be more scalable. One noniterative algorithm has good effectiveness and efficiency on graphs with medium size. Thus, depending on the requirements of different applications, the optimal choice of algorithms differs. This paper provides an empirical guideline for making such choices.

A fast two-stage algorithm for computing SimRank and its extensions

Abstract: Similarity estimation can be used in many applications such as recommender system, cluster analysis, information retrieval and link prediction. SimRank is a famous algorithm to measure objects' similarities based on link structure. We observe that if one node has no in-link, similarity score between this node and any of the others is always zero. Based on this observation, we propose a new algorithm, fast two-stage SimRank (F2S-SimRank), which can avoid storing unnecessary zeros and can accelerate the computation without accuracy loss. Under the circumstance of no accuracy loss, this algorithm uses less computation time and occupies less main memory. Experiments conducted on real and synthetic datasets demonstrate the effectiveness and efficiency of our F2S-SimRank.