Efficient Sphere-Effect Based Information Diffusion Prediction on Large-scale Social Networks

Information diffusion prediction is fundamental for forecasting user participation in information sharing on social networks, such as retweets on Twitter. Existing methods typically extract user relationships from social networks and historical interactions, while further capturing contextual information within the specific diffusion process. However, these methods have several limitations: (1) They often utilize sequential diffusion process for prediction and simplify differentiated influences among participants; (2) They capture user relationships on the entire graph for all users, in which most information is not necessary for a specific diffusion process and is too inefficient for real-world large-scale networks. To tackle these limitations, we propose a novel and scalable model SILN, for sphere-based information diffusion prediction on large social networks. Specifically, SILN features three components. First, we integrate two kinds of sphere effects in terms of structural and temporal views, which learn an enhanced cascade representation. Second, SILN designs an efficient learning scheme based on the cascade-specific subgraph, which significantly reduces the entire graph computation to smaller subgraphs. Third, to facilitate subgraph extraction, we develop an optimized graph storage technique to allow constant-time neighbor access and reduce the storage cost by about 30% in practice. Extensive experiments on six real-world datasets validate that SILN consistently outperforms seven state-of-the-art competitors in prediction performance while exhibiting exceptional time and space efficiency on million-node social networks.