Parallel Multicast Information Propagation Based on Social Influence

Most research on information propagation in social networks does not consider how to find information dissemination paths from the information source node to a set of influential nodes. In this paper, we introduce a multicast information propagation model which disseminates information from the information source node to a set of designated influential nodes in social networks, and formulate the problem with the objective to maximize the social influence on the information propagation paths. We then propose a Parallel Multicast information Propagation algorithm (PMP), which concurrently constructs a subgraph for each influential node, joins all the subgraphs into a merge graph, and finds the information propagation paths with the maximum social influence in the merge graph. The simulation results demonstrate that the proposed algorithm can achieve competitive performance in terms of the social influence on the information propagation paths.