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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.