SimRank

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

HowSim: A General and Effective Similarity Measure on Heterogeneous Information Networks

Abstract: Heterogeneous information networks (HINs) are usually used to model information systems with multi-type objects and relations. Measuring the similarity among objects is an important task in data mining applications. Currently, several similarity measures are defined for HIN. Most of these measures are based on meta-paths, which show sequences of node classes and edge types along the paths between two nodes. However, meta-paths, which are often designed by domain experts, are hard to enumerate and choose w.r.t. the quality of the similarity scores. This makes the existing similarity measures difficult to use in real applications. To address this problem, we extend SimRank, a well-known similarity measure for homogeneous graphs, to HINs, by introducing the concept of decay graph. The newly proposed relevance measure is called HowSim, which has the property of being meta-path free, and capturing the structural and semantic similarity simultaneously. The generality and effectiveness of HowSim, are demonstrated by extensive experiments.

GSimRank: A General Similarity Measure on Heterogeneous Information Network

Abstract: Measuring similarity of objects in information network is a primitive problem and has attracted many studies for widely applications, such as recommendation and information retrieval. With the advent of large-scale heterogeneous information network that consist of multi-type relationships, it is important to research similarity measure in such networks. However, most existing similarity measures are defined for homogeneous network and cannot be directly applied to HINs since different semantic meanings behind edges should be considered. This paper proposes GSimRank that is the extended form of the famous SimRank to compute similarity on HINs. Rather than summing all meeting paths for two nodes in SimRank, GSimRank selects linked nodes of the same semantic category as the next step in the pairwise random walk, which ensure the two meeting paths share the same semantic. Further, in order to weight the semantic edges, we propose a domain-independent edge weight evaluation method based on entropy theory. Finally, we proof that GSimRank is still based on the expected meeting distance model and provide experiments on two real world datasets showing the performance of GSimRank.

Enhancing link-based similarity through the use of non-numerical labels and prior information

Abstract: Several key applications like recommender systems require to compute similarities between the nodes (objects or entities) of a bipartite network. These similarities serve many important purposes, such as finding users sharing common interests or items with similar characteristics, as well as the automated recommendation and categorization of items. While a broad range of methods have been proposed to compute similarities in networks, such methods have two limitations: (1) they require the link values to be in the form of numerical weights representing the strength of the corresponding relation, and (2) they do not take into account prior information on the similarities. This paper presents a novel approach, based on the SimRank algorithm, to compute similarities between the nodes of a bipartite network. Unlike current methods, this approach allows one to model the agreement between link values using any desired function, and provides a simple way to integrate prior information on the similarity values directly in the computations. To evaluate its usefulness, we test this approach on the problem of predicting the ratings of users for movies and jokes.

Survey of Image Retrieval Techniques and Algorithms for Image-rich Information Networks

Abstract: Social networking sites allow users to share images, Ecommerce web sites also contains millions of images and thus forms image-rich information networks. Retrieving images from image-rich information networks is very challenging task, due to existence of information like text, user, image, feature, tags and group. The concept of image-rich information networks, image retrieval system and techniques like CBIR and TBIR are explained in this paper. The comparative study of image ranking and retrieval algorithms like simrank, k-simrank, HMOK-simrank is also mentioned in this paper. General Terms Image-rich information networks, CBIR, TBIR.

Node similarity calculation method based on SimRank

Abstract: The invention relates to a node similarity calculation method based on SimRank. The method comprises the steps as follows: 1) using an adjacent matrix form to express a multi-relational network; using non-iterative node similarity matrix S to express the node similarity of the multi-relational network; 2) establishing an Eigen-SimRank model and analyzing correlation matrix information needed to calculate node similarity matrix S; 3) calculating the node similarity in the multi-relational network according to the correlation matrix information needed to calculate node similarity matrix S if a network structure is not changed; 4) using an Eigen-SimRank dynamic update algorithm to update the correlation matrix information if the network structure is changed and calculating new correlation matrix information needed by a similarity matrix after obtaining the change of network structure; 5) calculating node similarity according to the updated correlation matrix information; 6) analyzing a similarity value among nodes in the multi-relational network according to a similarity calculation result obtained by calculating. The node similarity calculation method based on SimRank of the invention could be widely applied to the field of node similarity calculation in the network structure.