Information diffusion prediction aims to forecast the temporal spread of opinions and behaviors by identifying potential adopters. Existing methods typically treat information diffusion as a sequence of individual adoptions and rely on computationally expensive pairwise (one-to-one) influence computations, often restricting predictions to just the next adopter. This individual-level paradigm both misrepresents real-world collective (many-to-many) influences and suffers a critical efficiency trade-off: to remain feasible, such models must truncate long diffusion histories, thereby overlooking early initiators and opinion leaders. To overcome these limitations, we formalize a more practical task: Group-based Information Diffusion Prediction, and propose an effective and scalable GRID framework. Specifically, GRID first learns group-oriented graph embeddings via a task-regularized information bottleneck objective, which amplifies key influence pathways and produces reliable user embeddings for group identification. Built on these embeddings, the core GroupAttn module captures inter-group influence while reducing complexity from quadratic to linear in cascade length. This enables the modeling of ultra-long cascades (exceeding 10,000 users) without truncation while preserving representational fidelity within a provable error bound. Finally, a group-wise objective guides the model to predict semantically meaningful future groups. Extensive experiments on four real-world datasets show that GRID outperforms ten state-of-the-art baselines by an average of 10.65% in accuracy, while achieving an order-of-magnitude gain in efficiency and extending the supported cascade length by up to 10 times.

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.

Information diffusion prediction, which aims to forecast future infected users during the information spreading process on social platforms, is a challenging and critical task for public opinion analysis. With the development of social platforms, mass communication has become increasingly widespread. However, most existing methods based on GNNs and sequence models mainly focus on structural and temporal patterns in social networks, suffering from spurious diffusion connections and insufficient information for the diffusion analysis. We leverage strong reasoning capability of LLMs and develop a LL**M**-based causal framework for d**i**ffusion inf**l**uence **d**erivation (MILD). Comprehensively integrating four key factors of social diffusion, i.e., connections, active timelines, user profiles, and comments, MILD causally infers authentic diffusion links to construct a diffusion influence graph $G_I$. To validate the quality and reliability of our constructed graph $G_I$, we proposed a newly designed set of evaluation metrics w.r.t. diffusion prediction. We show MILD provides a reliable information diffusion structure that 12% absolutely better than the social network structure and achieves the state-of-the-art performance on diffusion prediction. MILD is expected to contribute to high-quality, more explainable, and more trustworthy public opinion analysis.