An Edge Contribution-Based Approach to Identify Influential Nodes from Online Social Networks

Social network can be viewed as a relationship between the set of connected entities, represented by a large graph consisting of vertices and edges. Dynamicity of the social network demands the isolation and/or incorporation of associated entities and represented by large graph. Depending on the application, some nodes of the graph play the influential roles accordingly and are classified as influential nodes. In this paper, we propose a novel method to identify the influential nodes by integrating Edge Contribution Factor (ECF) with the Clustering Coefficient (CC). Experimental results show that our proposed method efficiently selects the influential nodes from the large graph. The performance and the time complexity of the method show significant improvement over the other existing methods.