Future Link Prediction for Social Media Users with Community Detection Algorithm

Linking people around the globe is a very tedious task. Social Media plays a vital role in connecting people together with the help of social media websites such as Facebook, Twitter, Instagram, WhatsApp, Telegram, etc. A social network is very sparse because of its dense connections. It is critical to anticipate future connections between distinct users who can connect together. This work proposed a model to find out the exact relationship between users with similar interests. The proposed model entails determining who has more possibility to connect together on social networking sites according to the liking of people so that they can connect with their interests. Using centrality, the important vertices of a graph are analyzed. Centrality s works as a degree of a node that helps to maintain relationships with other nodes. The degree of a node is classified into two different ways such as in-degree (i.e., incoming edges from a node) and out-degree (i.e., outgoing edges from a node). This work helps to predict the link for connecting people of similar interest with the help of different parameters such as betweenness, edge-betweenness, centrality, and closeness centrality.