Maximizing the Effect of Information Adoption: A General Framework

With the development of social networking services, social influence analyses, as well as the influence maximization tasks, have attracted wide attention in both academia and industry. Traditional studies mainly focus on simulating process of influence spread. However, two basic functions of social spread, i.e., information propagation and information adoption have not been clearly distinguished. Usually, as information adoption could be even more significant for information publishers in application scenarios, more comprehensive analysis for effect of adoption is urgently required. To that end, in this paper, we propose a novel framework to generally describe social spread, in which information adoption process is separately formulated as random events. Along this line, when we apply this framework to the information adoption maximization task, with proving that the adoption maximization problem is NP-hard and submodular, we further design a polling-based algorithm to achieve an effective approximation. Extensive experiments on four real-world data sets demonstrate the effectiveness and efficiency of proposed algorithms, which validates that our approach could better summarize the complete social spread process, and further support the necessity of distinguishing information adoption from information propagation.