Detecting Seed Nodes of Information Diffusion in Online Social Networks Using a New Degree Centrality

Abstract

Information diffusion in online social networks has always some deterministic sources that are acting as the real sources of information diffusion in online social networks. This information that is getting diffused from some sources are making their ways in the formation of information cascades in online social networks .These cascades can be handled through various cascading models of information diffusion in online social networks. We in our research are going to uncover these information cascades as information dissemination trees in online social networks. If we are able to uncover a limited number of sources as limited real information generators in online social networks, we can generate a limited number of dissemination trees. Thus number of limited source nodes can be used further to handle the information diffusion cascades in online social networks using information dissemination trees. In this paper we are going to use a unique way of degree centrality of nodes to uncover the seed nodes in limited number with deterministic algorithm. We have tested our approach on three small social network datasets. Our method gives bests results and fits its best purpose for information tree generations when compared with the ordinary information dissemination tree generation in online social networks.