Stimulation Index of Cascading Transmission in Information Diffusion over Social Networks

摘要

Analyzing and modeling of information diffusion on social networks is essential because social networking sites (SNSs) have become crucial information infrastructures. In particular, "Influence Maximization," the extraction of information source nodes that deliver information to as many users as possible on a network, has been widely researched. However, actual information diffusion is caused not only propagation according to the network structure, but also a local rise in "trending" topics. We therefore focused on the edges that cause a chain of information transmission, regardless of the number of people who received the information. Based on the information cascade, where information is propagated in chains between nodes on a network, we propose the Stimulation Index to quantify how much edges affect the subsequent transmission of information. We also evaluate the proposed index using an artificial network and verify that it is effective.