With the rise of social media, accurately predicting information diffusion has become crucial for a wide range of applications. Existing methods usually employ sequential hypergraphs to model users' latent interaction preferences and use self-attention mechanisms to capture dependencies with users. However, they typically focus on a single temporal scale and lack the ability to effectively model temporal influence, which limits their performance in diffusion prediction tasks. To address these limitations, we propose a novel method (MHPS) to enhance information diffusion prediction via multiple granularity hypergraphs and a position-aware sequence model. Specifically, MHPS constructs hypergraph sequences of different granularities by grouping user interactions according to various time intervals. Additionally, to further enhance the modeling of temporal influence, two types of cross-attention mechanisms, namely next-step positional cross-attention and source influence cross-attention, are introduced within the cascade representation. The next-step positional cross-attention captures target position awareness, while the source influence cross-attention focuses on the impact of the initial source. Then, gating mechanisms and GRUs are employed to fuse the different attention outputs and predict the next target user. Extensive experiments on real-world datasets demonstrate that MHPS achieves competitive performance against state-of-the-art methods. The average improvements are up to 7.82% in terms of Hits@10 and 5.60% in terms of MAP@100. Our code is available at https://github.com/cgao-comp/MHPS.

Malicious social bot detection is vital for social network security. While graph neural networks (GNNs) based methods have improved performance by modeling structural information, they often overlook latent community structures, resulting in homogeneous node representations. Leveraging community structures, which capture discriminative group-level patterns, is therefore essential for more robust detection. In this paper, we propose a new Community-Aware Graph Contrastive Learning (CAGCL) framework for enhanced social bot detection. Specifically, CAGCL first exploits the latent community structures to uncover the potential group-level patterns. Then, a dual-perspective community enhancement module is proposed, which strengthens the structural awareness and reinforces topological consistency within communities, thereby enabling more distinctive node representations and deeper intra-community message passing. Finally, a community-aware contrastive learning module is proposed, which considers nodes within the same community as positive pairs and those from different communities as negative pairs, enhancing the discriminability of node representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that CAGCL consistently outperforms state-of-the-art baselines. The code is available at https://github.com/cgao-comp/.