Information diffusion prediction aims to predict the next infected user in the information diffusion, which is a critical task to understand how information spreads on social platforms. Existing methods mainly focus on the sequences or topology structure in euclidean space. However, they fail to sufficiently consider the hierarchical structure or power-law structure of the underlying topology of information cascade graphs and social networks, resulting in distortion of user features. To tackle above issue, we propose an innovative Constrained Temporal Hypergraphs and Graph Neural Networks (THGNets) framework that is tailored for information diffusion prediction. Specifically, we introduce hyperbolic temporal hypergraphs neural network to alleviate the distortion of user features by hyperbolic hierarchical learning in information cascades. Additionally, it also captures high-order dynamic interaction patterns between users and further integrates the time-consistency constraint mechanism to mitigate the instability and non-smoothness of user features in latent space. In parallel, we apply the hyperbolic graph neural network to investigate the hierarchical structure and user homogeneity on social networks, enhancing our understanding of social relationships. Moreover, hyperbolic gated recurrent units are employed to capture the potential dependency relationships between contextual users. Experiments conducted on four public datasets demonstrate that the proposed THGNets significantly outperform the existing methods, thereby validating the superiority and rationality of our approach.

Multimodal Emotion Recognition in Conversation (MERC) is a critical task for comprehending complex emotions from multiple sources in human-computer interaction. Despite existing methods having made progress, they still struggle to capture hierarchy characteristics and high-order interactions between modalities. Recent research adopts Euclidean space geometry for multimodal representation learning and fusion, resulting in representation and distance distortions. To tackle these challenges, we propose a multimodal hyperbolic embedding and hyperbolic hypergraph fusion framework. A hyperbolic embedding learning module projects modality representations into the Poincaré ball model to perceive hierarchies. To enhance representation discriminative and semantic consistency, it incorporates radial and angular contrastive learning objectives. A hyperbolic hypergraph fusion module further exploits high-order latent relationships and improves fusion results. Experimental results demonstrate that the proposed framework outperforms state-of-the-art methods and augments stability on IEMOCAP and MELD datasets.

With the rapid development of 5g, artificial intelligence, chip technology (GPU computing power), and capital investment, human body synthesis technology is entering a growth stage. According to people’s comprehensive image, we divide them into idol school, academic school, and practical school; According to the driving methods of virtual human synthesis, we divide it into avatar type, voice or text content driving type, and human-computer interaction type. We recently introduced the relevant model of the synthesis method and the implementation process of virtual human lip synthesis and explained the synthesis process. We discussed voice2face, the latest scheme for real-time synthesis of virtual human, but this method still can not synthesize high-quality video in milliseconds.