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 38 articles )

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  1. Ng, Sio Wan; Lei, Siu-Long; Lu, Juan; Gong, Zhiguo: Speeding up SimRank computations by polynomial preconditioners (2020)
  2. Yu, Liqin; Cao, Fuyuan; Zhao, Xingwang; Yang, Xiaodan; Liang, Jiye: Combining attribute content and label information for categorical data ensemble clustering (2020)
  3. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  4. Sudo, Kotaro; Osugi, Naoya; Kanamori, Takafumi: Numerical study of reciprocal recommendation with domain matching (2019)
  5. Xu, Guangluan; Wang, Xiaoke; Wang, Yang; Lin, Daoyu; Sun, Xian; Fu, Kun: Edge-nodes representation neural machine for link prediction (2019)
  6. Balelli, Irene; Milišić, Vuk; Wainrib, Gilles: Random walks on binary strings applied to the somatic hypermutation of B-cells (2018)
  7. 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)
  8. Boongoen, Tossapon; Iam-On, Natthakan: Cluster ensembles: a survey of approaches with recent extensions and applications (2018)
  9. Li, Zhenpeng; Shang, Changjing; Shen, Qiang: Inter-variable correlation prediction with fuzzy connected-triples (2018)
  10. Zhang, Mingxi; Wang, Jinhua; Wang, Wei: HeteRank: a general similarity measure in heterogeneous information networks by integrating multi-type relationships (2018)
  11. Eades, Peter; Hong, Seok-Hee; Nguyen, An; Klein, Karsten: Shape-based quality metrics for large graph visualization (2017)
  12. Guerini, Mattia; Moneta, Alessio: A method for agent-based models validation (2017)
  13. Li, Ruiqi; Zhao, Xiang; Shang, Haichuan; Chen, Yifan; Xiao, Weidong: Fast top-(k) similarity join for SimRank (2017)
  14. Moradabadi, Behnaz; Meybodi, Mohammad Reza: Link prediction based on temporal similarity metrics using continuous action set learning automata (2016)
  15. Wu, Zhihao; Lin, Youfang; Wan, Huaiyu; Jamil, Waleed: Predicting top-(L) missing links with node and link clustering information in large-scale networks (2016)
  16. Yang, Liang; Liu, Bing; Lin, Hongfei; Lin, Yuan: Combining local and global information for product feature extraction in opinion documents (2016) ioport
  17. Zhang, Yinglong; Li, Cuiping; Xie, Chengwang; Chen, Hong: Accuracy estimation of link-based similarity measures and its application (2016)
  18. Du, Lingxia; Li, Cuiping; Chen, Hong; Tan, Liwen; Zhang, Yinglong: Probabilistic SimRank computation over uncertain graphs (2015)
  19. Gleich, David F.: PageRank beyond the web (2015)
  20. Huang, Xin; Cheng, Hong; Yu, Jeffrey Xu: Dense community detection in multi-valued attributed networks (2015)

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