March 14, 2024, 4:42 a.m. | Samarth Khanna, Sree Bhattacharyya, Sudipto Ghosh, Kushagra Agarwal, Asit Kumar Das

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.08613v1 Announce Type: cross
Abstract: The exponential growth in scale and relevance of social networks enable them to provide expansive insights. Predicting missing links in social networks efficiently can help in various modern-day business applications ranging from generating recommendations to influence analysis. Several categories of solutions exist for the same. Here, we explore various feature extraction techniques to generate representations of nodes and edges in a social network that allow us to predict missing links. We compare the results of …

abstract analysis applications arxiv business business applications cs.ai cs.lg cs.si features growth influence insights link prediction modern networks prediction recommendations representation representation learning scale social social networks solutions them type

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