SimRank: a measure of structural-context similarity. The problem of measuring ”similarity” of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says ”two objects are similar if they are related to similar objects:” This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.

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  1. Chen, Zhen-Yu; Fan, Zhi-Ping; Sun, Minghe: Tensorial graph learning for link prediction in generalized heterogeneous networks (2021)
  2. Lu, Juan; Gong, Zhiguo; Yang, Yiyang: A matrix sampling approach for efficient SimRank computation (2021)
  3. Comin, Cesar H.; Peron, Thomas; Silva, Filipi N.; Amancio, Diego R.; Rodrigues, Francisco A.; Costa, Luciano da F.: Complex systems: features, similarity and connectivity (2020)
  4. Kumar, Ajay; Singh, Shashank Sheshar; Singh, Kuldeep; Biswas, Bhaskar: Link prediction techniques, applications, and performance: a survey (2020)
  5. Ng, Sio Wan; Lei, Siu-Long; Lu, Juan; Gong, Zhiguo: Speeding up SimRank computations by polynomial preconditioners (2020)
  6. Yu, Liqin; Cao, Fuyuan; Zhao, Xingwang; Yang, Xiaodan; Liang, Jiye: Combining attribute content and label information for categorical data ensemble clustering (2020)
  7. Jaeger, Manfred; Lippi, Marco; Pellegrini, Giovanni; Passerini, Andrea: Counts-of-counts similarity for prediction and search in relational data (2019)
  8. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  9. Sudo, Kotaro; Osugi, Naoya; Kanamori, Takafumi: Numerical study of reciprocal recommendation with domain matching (2019)
  10. Balelli, Irene; Milišić, Vuk; Wainrib, Gilles: Random walks on binary strings applied to the somatic hypermutation of B-cells (2018)
  11. Ballweg, Kathrin; Pohl, Margit; Wallner, Günter; von Landesberger, Tatiana: Visual similarity perception of directed acyclic graphs: a study on influencing factors and similarity judgment strategies (2018)
  12. Boongoen, Tossapon; Iam-On, Natthakan: Cluster ensembles: a survey of approaches with recent extensions and applications (2018)
  13. Chen, Dongming; Zhao, Wei; Wang, Dongqi; Huang, Xinyu: Similarity-based local community detection for bipartite networks (2018)
  14. Li, Zhenpeng; Shang, Changjing; Shen, Qiang: Inter-variable correlation prediction with fuzzy connected-triples (2018)
  15. Zhang, Mingxi; Wang, Jinhua; Wang, Wei: HeteRank: a general similarity measure in heterogeneous information networks by integrating multi-type relationships (2018)
  16. Eades, Peter; Hong, Seok-Hee; Nguyen, An; Klein, Karsten: Shape-based quality metrics for large graph visualization (2017)
  17. Guerini, Mattia; Moneta, Alessio: A method for agent-based models validation (2017)
  18. Li, Ruiqi; Zhao, Xiang; Shang, Haichuan; Chen, Yifan; Xiao, Weidong: Fast top-(k) similarity join for SimRank (2017)
  19. Janssen, Jeannette; Prałat, Paweł; Wilson, Rory: Nonuniform distribution of nodes in the spatial preferential attachment model (2016)
  20. Moradabadi, Behnaz; Meybodi, Mohammad Reza: Link prediction based on temporal similarity metrics using continuous action set learning automata (2016)

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