SimRank

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.


References in zbMATH (referenced in 44 articles )

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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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