• Avrodip Kumar Babai
• Mr. Mukesh Sharma
• Ms. Prashansa Taneja

Link prediction in social networks is critical for mining and analysing the evolution of social networks because it predicts missing links in current infrastructure and digital or dissolution links in future networks. According to previous research, many similarity measurement techniques employ either node neighbourhood information or shared profile traits to calculate similarity between unconnected members in an online social network. There is no approach for link prediction that takes into account both node neighbourhood information and common profile features for calculating similarity. In this chapter, we discuss the specifics of our proposed link prediction approach, FINN (Feature Integration with Node Neighbor), which leverages neighborhood-based and attribute-based profile information to forecast missing connections.

Link Prediction, Social Network, Similarity Metric, Dynamic Network, Learning Model

"A Study on Link Prediction in Networks: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.g475-g483, June-2022, Available :http://www.jetir.org/papers/JETIR2206655.pdf

"A Study on Link Prediction in Networks: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppg475-g483, June-2022, Available at : http://www.jetir.org/papers/JETIR2206655.pdf