Abstract
Complex relationships in many real-world problems can be represented as networks where nodes represent individuals and relationships among them are represented by links. So, one of the key issues in such networks is the evolution or creation of edges. Link prediction is the solution where it finds the missing links (edges) in a static case (in a given snapshot of a network) or future links in a dynamic case (given several snapshots of networks at different time instants). In this experimental work, we consider the weighted versions of different existing algorithms and showed their performance on several real networks of different domains. We observed that the weighted version of the method path of length 3, i.e. L3-WT outperforms other methods on all four evaluation metrics with some exceptions. LHN1-WT method is the second outperformer on LesMiserables and Netscience datasets.
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Notes
- 1.
In this article, we use links and edges interchangeably.
- 2.
In the article, we use networks and datasets interchangeably.
- 3.
0.1 Fraction of test data means 10% of the total data is taken as test set.
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Kumar, A., Singh, S.S., Mishra, S. (2023). Empirical Analysis of Unsupervised Link Prediction Algorithms in Weighted Networks. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_15
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