Topic
SimRank
About: SimRank is a research topic. Over the lifetime, 250 publications have been published within this topic receiving 21163 citations.
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04 Sep 2020
TL;DR: A novel similarity model, namely RoleSim*, is proposed, which accurately evaluates pairwise role similarities in a more comprehensive manner and achieves higher accuracy than its competitors while retaining comparable computational complexity bounds of RoleSim.
Abstract: RoleSim and SimRank are popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same.
TL;DR: A novel low-rank approximation of SimRank is proposed, a well-known similarity measure between graph vertices, and its application in graph supervised learning is proposed.
01 Jan 2018
TL;DR: The goal of the presented system is to identify how the user- item ratings can affect in user friendship relations to make a correct recommendation and the carried out experimental analysis used to evaluate the accuracy of the system.
Abstract: This paper presents a recommender system based on a game theory in which the recommendations are made from user-item ratings. The user-item ratings are the most essential factor for a social network to maintain its social relationships among users. It is not possible for a social network to force all of its users to rate items and such techniques are not formed yet. In this paper, game theory and SimRank (Similarity Based on Random Walk) are used as a core algorithm to build the recommender system. The user-item ratings dataset is decomposed into similar groups based on the user ratings by the game theory. The similarities among the ’similar interest’ users are calculated with the SimRank algorithm. Based on the user similarity information, user profile and rating dataset, the presented system would provide proper recommendation of items to its users. The goal of the presented system is to identify how the user- item ratings can affect in user friendship relations to make a correct recommendation and the carried out experimental analysis used to evaluate the accuracy of the system.
TL;DR: This work proposes a novel framework named SEGNN, which aims at finding and using the sparse representation knowledge to improve the result of image detection and applies a novel SimRank method, to justify the rationality of the semantic reasoning.
12 Aug 2020
TL;DR: A new similarity measurement called LSimRank is proposed which measures the similarities among nodes by using both the structural information and the label information of a graph, and is superior over SimRank and other variants on labeled graphs.
Abstract: The notion of node similarity is useful in many real-world applications. Many existing similarity measurements such as SimRank and its variants have been proposed. Among these measurements, most capture the structural information of a graph only, and thus they are not suitable for graphs with additional label information. We propose a new similarity measurement called LSimRank which measures the similarities among nodes by using both the structural information and the label information of a graph. Extensive experiments on datasets verify that LSimRank is superior over SimRank and other variants on labeled graphs.