A novel influence maximization algorithm for a competitive environment based on social media data analytics

Online social networks are increasingly connecting people around the world. Influence maximization is a key area of research in online networks, which identifies influential users during information dissemination. Most existing influence methods only consider transmission single channel, but real-world mostly include multiple channels with competitive relationships. The problem an environment involves selecting seed node set for certain information, so that it can avoid other and ultimately affect largest nodes network. In this paper, calculation achieved according to local community discovery algorithm, based on dispersion characteristics dynamic structure. Furthermore, considering two various dissemination cases as example, solution designed self-interested assumption known, novel algorithm avoidance user interest proposed. Experiments conducted Twitter dataset demonstrates efficiency our proposed terms accuracy time against notable algorithms.