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A novel similarity measure for the link prediction in unipartite and bipartite networks

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Abstract

Networks are everywhere and their applications in various fields such as computer science, biological science, economics, and chemical engineering attracted attention of many researchers. Many complex systems in the real world can be represented by networks or graphs. Link prediction is one of the most important tasks in network analysis, thus attracting tremendous research interests in the last decades. In this paper, we present a novel algorithm for link prediction that works efficiently for both unipartite graphs and bipartite graphs. Our novel algorithm is based on the concept of eigenvectors and shortest distance between the nodes. We used the Peron–Frobenius theorem of node importance for link prediction. Four metrics, namely AUC, precision, prediction-power, and precision@K, were computed and compared with fourteen baseline algorithms to test the performance of the proposed algorithm. Testing was done on the thirteen datasets, and experimental results show that the proposed method outperforms the baseline algorithm on the basis of four given metrics.

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Acknowledgements

The authors would like to express gratitude to Prof. Karmeshu for helpful discussions and suggestions on earlier drafts of this research paper.

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  1. Department of Computer Science and Engineering, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh, India

    Purushottam Kumar & Dolly Sharma

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  1. Purushottam Kumar
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  2. Dolly Sharma
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Correspondence to Dolly Sharma.

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Kumar, P., Sharma, D. A novel similarity measure for the link prediction in unipartite and bipartite networks. Soc. Netw. Anal. Min. 11, 41 (2021). https://doi.org/10.1007/s13278-021-00745-z

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