SimRank

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

SimRank Computation on Uncertain Graphs

Abstract: SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics. Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities. However, the existing SimRank computation algorithms either overlook uncertainty in graph structures or is based on an unreasonable assumption (Du et al). In this paper, we study SimRank similarities on uncertain graphs based on the possible world model of uncertain graphs. Following the random-walk-based formulation of SimRank on deterministic graphs and the possible worlds model of uncertain graphs, we define random walks on uncertain graphs for the first time and show that our definition of random walks satisfies Markov's property. We formulate the SimRank measure based on random walks on uncertain graphs. We discover a critical difference between random walks on uncertain graphs and random walks on deterministic graphs, which makes all existing SimRank computation algorithms on deterministic graphs inapplicable to uncertain graphs. To efficiently compute SimRank similarities, we propose three algorithms, namely the baseline algorithm with high accuracy, the sampling algorithm with high efficiency, and the two-phase algorithm with comparable efficiency as the sampling algorithm and about an order of magnitude smaller relative error than the sampling algorithm. The extensive experiments and case studies verify the effectiveness of our SimRank measure and the efficiency of our SimRank computation algorithms.

Friendship Link Recommendation Based on Content Structure Information

Abstract: Intuitively, a friendship link between two users can be recommended based on the similarity of their generated text content or structure information. Although this problem has been extensively studied, the challenge of how to effectively incorporate the information from the social interaction and user generated content remains largely open. We propose a model (LRCS) to recommend user’s potential friends by incorporating user’s generated content and structure features. First, network users are clustered based on the similarity of user’s interest and structural features. Users in the same cluster with the query user are considered as the candidate friends. Then, a weighted SimRank algorithm is proposed to recommend the most similar users as the friends. Experiments on two real-life datasets show the superiority of our approach.

A collaborative filtering recommendation algorithm based on weighted SimRank and social trust

Abstract: Collaborative filtering is one of the most widely used recommendation technologies, but the data sparsity and cold start problem of collaborative filtering algorithms are difficult to solve effectively. In order to alleviate the problem of data sparsity in collaborative filtering algorithm, firstly, a weighted improved SimRank algorithm is proposed to compute the rating similarity between users in rating data set. The improved SimRank can find more nearest neighbors for target users according to the transmissibility of rating similarity. Then, we build trust network and introduce the calculation of trust degree in the trust relationship data set. Finally, we combine rating similarity and trust to build a comprehensive similarity in order to find more appropriate nearest neighbors for target user. Experimental results show that the algorithm proposed in this paper improves the recommendation precision of the Collaborative algorithm effectively.

Parallel Simrank Computing on Large Scale Dataset on Mapreduce

Abstract: Many fields need computing the similarity between objects, such as recommendation system, search engine etc Simrank is one of the simple and intuitive algorithms It is rigidly based on the random walk theorem There are three existing iterative ways to compute simrank, however, all of them have one problem, that is time consuming; moreover, with the rapidly growing data on the Internet, we need a novel parallel method to compute simrank on large scale dataset Hadoop is one of the popular distributed platforms This paper combines the features of the Hadoop and computes the simrank parallel with different methods, and compars them in the performance