Multi-modal knowledge graph (MMKG) completion aims to infer missing links in knowledge graphs by leveraging both structural information and multi-modal data, such as images and text. While existing methods focus on multi-modal feature fusion, they often fail to disentangle modality-specific and shared features, leading to redundant and suboptimal embeddings. Furthermore, implicit semantic correlations between entities are frequently overlooked. To address these issues, we propose INCADE, an Implicit Entity Community-Aware Disentangled Expert framework that jointly captures implicit high-order correlations among entities and disentangles modality-specific and shared representations. INCADE introduces an entity community-aware encoder to model implicit entity interactions and auxiliary tasks (e.g., modality memorization and unlearning) to enforce disentanglement. This advantage enables the model to easily utilizes both explicit and implicit multi-modal information. Extensive experiments on standard MMKG benchmarks demonstrate that INCADE achieves state-of-the-art performance, outperforming 14 baseline models. Code is available at https://github.com/shchli/INCADE.

Information diffusion prediction is a vital component for a wide range of social applications, including viral marketing identification and personal recommendation. Prior methods primarily focus on learning target user representation by modeling contextual information from the historical retweet user sequence of a single cascade, overlooking the uncertainties that exist in both historical propagation trajectory and future diffusion trends. In this work, we propose DucDiff, a novel dual-consistent diffusion model for enhancing target user representation used for information diffusion prediction. DucDiff harnesses the distribution generation capability of the diffusion model to generate target user representations from a distributional perspective rather than a fixed vector. Specifically, it captures the multi-latent aspects (i.e., uncertainties) of target user representation from historical and future user sequences, respectively, using disentangled dual denoising modules. Additionally, a shared information bottleneck is designed for the cross-distillation of knowledge between the historical and future denoising modules, eliminating the performance gap between training and inference, while ensuring that future information can be implicitly introduced during the inference phase. Extensive experiments conducted on five datasets demonstrate that DucDiff significantly outperforms state-of-the-art baselines.

Information diffusion prediction is a critical task for many social network applications. However, current methods are mainly limited by the following aspects: user relationships behind resharing behaviors are complex and entangled. To address these issues, we propose MHGFormer, a novel multi-channel hypergraph transformer framework, to better decouple complex user relations and obtain fine-grained user representations. First, we employ designed triangular motifs to decouple user relations into three different level hypergraphs. Second, a position-aware hypergraph transformer is used to refine user relation and obtain high-quality user representations. Extensive experiments conducted on two social datasets demonstrate that MHGFormer outperforms state-of-the-art diffusion models across several settings.