Community analysis of influential nodes for information diffusion on a social network

We consider the problem of finding influential nodes for information diffusion on a social network under the independent cascade model. It is known that the greedy algorithm can give a good approximate solution for the problem. Aiming to obtain efficient methods for finding better approximate solutions, we explore what structural feature of the underlying network is relevant to the greedy solution that is the approximate solution by the greedy algorithm. We focus on the SR-community structure, and analyze the greedy solution in terms of the SR-community structure. Using real large social networks, we experimentally demonstrate that the SR-community structure can be more strongly correlated with the greedy solution than the community structure introduced by Newman and Leicht.