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A new similarity measure for link prediction based on local structures in social networks

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  • Aghabozorgi, Farshad
  • Khayyambashi, Mohammad Reza

Abstract

Link prediction is a fundamental problem in social network analysis. There exist a variety of techniques for link prediction which applies the similarity measures to estimate proximity of vertices in the network. Complex networks like social networks contain structural units named network motifs. In this study, a newly developed similarity measure is proposed where these structural units are applied as the source of similarity estimation. This similarity measure is tested through a supervised learning experiment framework, where other similarity measures are compared with this similarity measure. The classification model trained with this similarity measure outperforms others of its kind.

Suggested Citation

  • Aghabozorgi, Farshad & Khayyambashi, Mohammad Reza, 2018. "A new similarity measure for link prediction based on local structures in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 12-23.
    • Handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:12-23
    DOI: 10.1016/j.physa.2018.02.010
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    References listed on IDEAS

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    Cited by:

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    4. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    5. Yao, Yabing & Zhang, Ruisheng & Yang, Fan & Tang, Jianxin & Yuan, Yongna & Hu, Rongjing, 2018. "Link prediction in complex networks based on the interactions among paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 52-67.
    6. Yanxin Liu & Huajiao Li & Jianhe Guan & Xueyong Liu & Yajie Qi, 2019. "The role of the world’s major steel markets in price spillover networks: an analysis based on complex network motifs," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 697-720, December.
    7. Chi, Kuo & Qu, Hui & Yin, Guisheng, 2022. "Link prediction for existing links in dynamic networks based on the attraction force," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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