MDER: modified degree with exclusion ratio algorithm for influence maximisation in social networks

The online social network has become an integral part of our day to life and serves as an excellent platform for sharing ideas, opinions, and products. Influence maximization (IM) is a widely studied topic in the area of social network analysis. The objective of IM is to find influential nodes that can disseminate information to a larger extent in the network. Many local and global centrality measures are proposed to rank the nodes based on their spreading capability with certain limitations. Many proposed algorithms locate the spreaders sharing overlapping regions or are closely placed, which may cause interference in spreading. In this paper, based on the notion of maximum coverage of the information and minimum interference in spreading, we propose a novel semi-local algorithm named as modified degree centrality with exclusion ratio to identify influential nodes from diverse locations in the network. We use modified degree centrality by considering neighbours upto 2-hops and introduce the novel idea of exclusion ratio to ensure minimum overlapping between regions influenced by the chosen spreader nodes. Extensive experimental validation using classical information diffusion model demonstrates that the proposed method delivers better results in comparison to many popular contemporary methods of influence maximization.