Information Diffusion Prediction With Augmented Diffusion Dependency and Multigranularity Temporal Influence

Information diffusion prediction plays a pivotal role in the analysis of information propagation across social networks. Many existing methods rely on learning social homophily solely from users’ social connections as a single diffusion dependency to drive information diffusion. Moreover, these approaches often capture temporal influence from cascades within discrete time intervals, which might be inadequate in describing complex diffusion processes and can limit prediction performance. To overcome these limitations, we propose a novel approach with augmented diffusion dependency and multigranularity temporal influence (ADDMT) for information diffusion prediction. Our method strategically leverages the interactive regularity implicit in historical diffusion cascades. This information is integrated with social homophily through a cross-graph convolution network (GCN) to augment the diffusion dependency among users. Furthermore, we introduce multiple overlapping sliding windows to partition diffusion cascades. Adjacent cascade slices exhibit 50% overlap, enhancing semantic and structural coherence. In addition, we employ the combination of hypergraph convolution networks (HGCNs) and temporal convolution networks (TCNs) to capture multigranularity temporal influence within cascades. This design enables our model to further discern evolutionary trends and ephemeral fluctuations in users’ preferences across time intervals. The experimental results, obtained from comprehensive evaluations on four realistic datasets, demonstrate the superior performance of our proposed model. In particular, our model surpasses previous state-of-the-art diffusion prediction models, as evidenced by improved metrics such as Hits@K and MAP@K. These results underscore the effectiveness and robustness of ADDMT in predicting information diffusion in social networks.