Node Classification and Link Prediction in Social Graphs using RLVECN

Node classification and link prediction problems in Social Network Analysis (SNA) remain open research problems with respect to Artificial Intelligence (AI). Inherent representations about social network structures can be effectively harnessed for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. In this paper, we have proposed a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition is designed for analyzing and extracting expressive feature representations from social network structures to aid in link prediction, node classification and community detection tasks. RLVECN utilizes an edge sampling technique for exploiting features of a given social network via learning the context of each actor with respect to its associate actors.