Time Based Constrained Object Identification in a Dynamic Social Network

Abstract

Social networking has become an ubiquitous part of communication in this modern era. Categorization in dynamic social network is very challenging because of the key feature of social networks – continual change. A typical social network grows exponentially over time, as new activities and interactions evolve rapidly. The dynamic social networks are efficient in modeling the behavior of any real life interactions. In this paper, two algorithms are considered, of which one helps to identify the exact community to which a new node belongs. The second algorithm uses a rule based approach to identify the exact community which satisfies a set of constraints. The social network is conceived as a weighted directed graph consisting of a set of nodes, edges , node weights and edge weights. The node weights are computed based on the node rank algorithm and the edge weights based on the people rank algorithm. In the proposed algorithms, the dynamic attributes of the nodes and time-based constraints play a crucial role in the identification of categories in the social network. The main objective of the algorithm is to obtain an optimal community network with high accuracy by using a heuristic approach. This approach helps to categorize a newly added node or entity to one or more communities dynamically.