Link Prediction in Social Networks using Vertex Entropy
Shubham1, Rajeev Kumar2, Naveen Chauhan3
1Shubham, Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, (Himachal Pradesh), India.
2Dr. Rajeev Kumar, Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, (Himachal Pradesh), India.
3Dr. Naveen Chauhan, Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, (Himachal Pradesh), India.
Manuscript received on 07 June 2023 | Revised Manuscript received on 19 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 133-139 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.A75930512123 | DOI: 10.35940/ijrte.A7593.0712223
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Abstract: Many link prediction methods have been put out and tested on several actual networks. The weights of linkages are rarely considered in these studies. Taking both the network’s structure and link weight into account is required for link prediction. Previous researchers mostly overlooked the topological structure data in favour of the naturally occurring link weight. With the use of the concept of entropy, a new link prediction algorithm has been put forth in this paper. When used in real-time social networks, this algorithm outperforms the industry standard techniques. This paper concentrated on both topological structural information which focuses on calculating the vertex entropy of each very vertex and link weight in the proposed method. Both weighted and unweighted networks can benefit from the proposed method. Unipartite and bipartite networks can also use the suggested methods. Further, results demonstrate that the proposed method performs better than competing or traditional strategies, particularly when targeted social networks are sufficiently dense.
Keywords: Ego Network, Social Network, Link Prediction, Sociogram.
Scope of the Article: Social Network