Computational solution of networks versus cluster grouping for social network contact recommender system

Abstract

Graphs have become the dominant life-form of many tasks as they advance a structure to represent many tasks and the corresponding relations. A powerful role of networks/graphs is to bridge local feats that exist in vertices as they blossom into patterns that help explain how nodal relations and their edges impacts a complex effect that ripple via a graph. User cluster are formed as a result of interactions between entities. Many users can hardly categorize their contact into groups today such as “family”, “friends”, “colleagues” etc. Thus, the need to analyze such user social graph via implicit clusters, enables the dynamism in contact management. Study seeks to implement this dynamism via a comparative study of deep neural network and friend suggest algorithm. We analyze a user’s implicit social graph and seek to automatically create custom contact groups using metrics that classify such contacts based on a user’s affinity to contacts. Experimental results demonstrate the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.