SimRank: a measure of structural-context similarity

By Glen Jeh, Jennifer Widom. Knowledge Discovery and Data Mining, 2002

2,280 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/809e37b8f5a60038a04ab6bc88ff1a81a6ef3f09

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Citations in the corpus

Graph Clustering Based on Structural/Attribute Similarities (2009, background): “Jeh and Widom [12] designed a measure called SimRank, which deﬁnes the similarity between two vertices in a graph by their neighborhood similarity.”
Reinforced Similarity Integration in Image-Rich Information Networks (2013, neutral): “Index Terms—Information retrieval, image mining, information network, ranking Ç”
Efficient semi-streaming algorithms for local triangle counting in massive graphs (2008, methodology): “Finally, in [19], the authors apply similar techniques to produce indices of page similarity that extend SimRank [26].”
Iteratively clustering web images based on link and attribute reinforcements (2005, positive): “The effectiveness of the former two assumptions has been proved by many previous approaches addressing relational data [9][20].”
Weisfeiler-Lehman Neural Machine for Link Prediction (2017, background): “SimRank score is a recursive definition. l + xy is the ( x , y ) entry of the pseudoinverse of the graph’s Laplacian matrix. outperforms state-of-the-art link prediction methods and offers great universality across various networks.”
A Survey of Link Prediction in Complex Networks (2016, methodology): “SimRank is a method that computes how soon two random walkers starting from nodes x and y are expected to meet [Jeh and Widom 2002].”
Personalized entity recommendation: a heterogeneous information network approach (2014, background): “Studies regarding entity similarity measurements, as a fundamental technique, have been actively engaged in many research works as well [4] [11] [24].”
Recommender Systems (2016, positive): “The topic-sensitive PageRank algorithm is described in [243], and the SimRank algorithm is described in [278].”
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (2020, methodology): “ximity, is that vertices closely connected should be considered similar. Early neighborhood similarity measures include Jaccard similarity (counting common neighbors), RWR similarity [36] and SimRank [21], etc. Most recently developed network embedding algorithms, such as LINE [48], DeepWalk [39], n”
Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding (2017, methodology): “Earlier graph proximity search work such as Personalized PageRank (Jeh and Widom 2003) and SimRank (Jeh and Widom 2002) does not differentiate semantic classes.”
Random walk with restart: fast solutions and applications (2008, methodology): “Many applications take random walk and related methods as the building block, including PageRank [23], personalized PageRank [13], SimRank [15], neighborhood formulation in bipartite graphs [28], content-based image retrieval [14], cross modal correlation discovery [25], the BANKS system [2], Object”
Fast Random Walk Graph Kernel (2012, background): “There are also many works on measuring the similarity between nodes on the same graph, e.g., random walk with restart [27, 39], sinkaugmented delivered current [8], cycle free effective conductance [22], survivable network [13], direction-aware proximity [38], ObjectRank [2], RelationalRank [11], Si”
Recommender Systems (2012, positive): “This allows us to define SimRank in a self-consistent way [154] as s xy = C ∑ z∈Γx ∑ z′∈Γy s SimRank zz′ kxky , (45) where sxx = 1 and C ∈ [0, 1] is a free parameter.”