SimRank and its variants in academic literature data

Hamedani, Masoud Reyhani; Kim, Sang–Wook

doi:10.1145/2851613.2851811

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…One of the most popular metrics is SimRank [25], which iteratively computes similarity scores based on the hypothesis that two nodes are similar if they link to similar nodes. Different extensions of SimRank have been proposed [26]. SimRank recursively computes the similarity of two nodes according to the average similarity of all their neighbor pairs, which can also be interpreted, as suggested by its original authors, as how soon two random walkers will meet if they start from these nodes.…”

Section: Related Workmentioning

confidence: 99%

An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

2021

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Role is a fundamental concept in the analysis of the behavior and function of interacting entities in complex networks. Role discovery is the task of uncovering the hidden roles of nodes within a network. Node roles are commonly defined in terms of equivalence classes. Two nodes have the same role if they fall within the same equivalence class. Automorphic equivalence, where two nodes are equivalent when they can swap their labels to form an isomorphic graph, captures this notion of role. The binary concept of equivalence is too restrictive, and nodes in real-world networks rarely belong to the same equivalence class. Instead, a relaxed definition in terms of similarity or distance is commonly used to compute the degree to which two nodes are equivalent. In this paper, we propose a novel distance metric called automorphic distance, which measures how far two nodes are from being automorphically equivalent. We also study its application to node embedding, showing how our metric can be used to generate role-preserving vector representations of nodes. Our experiments confirm that the proposed automorphic distance metric outperforms a state-of-the-art automorphic equivalence-based metric and different state-of-the-art techniques for the generation of node embeddings in different role-related tasks.

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Section: Related Workmentioning

confidence: 99%

An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

2021

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“…Despite the current success of SimRank, authors claim that it has some problems that negatively affect its effectiveness in similarity computation. Hamedani and Kim [2016] discuss existing problems of SimRank, present SimRank variants that have been proposed to solve those problems, and evaluate the effectiveness of SimRank and its variants in similarity computation for academic literature.…”

Section: Other Modelsmentioning

confidence: 99%

Random Walks on the Reputation Graph

Ribas

Ribeiro-Neto

Santos

et al. 2015

Proceedings of the 2015 International Conference on the Theory of Information Retrieval

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world know how creative you are. I know you made everything you could to take care of us. Thanks for everything, may God bless you in heaven. Sávio, my brother, funny guy, clever man, thanks for showing me that there is no limit on what we can achieve if we love what we do and if we are committed to reaching the next level of excellence. Your award-winning mathematical thinking influences many people around you. I am proud to be your brother. Who else can say that received a medal from the president of the country? Also, thanks for finishing your Ph.D. precisely on the same day and time as me, and not before. Otherwise, I would have to explain to people why my younger brother started later and finished before. Karine, thanks for being a good friend and supporting Sávio in his journey. It is great having you in the family. Lili, thanks for always helping us carefully and teach us math. Reginaldo, my journey in computer science started with you saying to me when I was a child: "Hey Sabir, computers are the future. You should check it.". That triggered my curiosity about the field. Samuel and Alberto, thanks for being close friends and for all the discussions we had about life.

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Computing User Similarity by Combining SimRank++ and Cosine Similarities to Improve Collaborative Filtering

Wang

Xia³

et al. 2017

2017 14th Web Information Systems and Applications Conference (WISA)

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SimRank and its variants in academic literature data

Cited by 5 publications

References 20 publications

An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

An Automorphic Distance Metric and Its Application to Node Embedding for Role Mining

Random Walks on the Reputation Graph

Computing User Similarity by Combining SimRank++ and Cosine Similarities to Improve Collaborative Filtering

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