.png){kind=icon}
返回
H-Diffu: Hyperbolic Representations for Information Diffusion Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2022.3209067.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the proliferation of online social networks, a great deal of online user action data has been generated. Such data has enabled the study of information diffusion prediction, which is a fundamental problem for understanding the propagation of information on social media platforms. In diffusion prediction models, there are two standard components, i.e., a social graph and information diffusion cascades. We observe that both components exhibit latent hierarchical structures. However, most existing models are designed based on euclidean spaces, and hence cannot effectively capture complex patterns, especially hierarchical structures. Therefore, we investigate a novel research problem to learn hyperbolic representations for information diffusion prediction. To reflect the different characteristics of social graphs and diffusion cascades, we encode them into two latent hyperbolic spaces with different trainable curvatures. In addition, to model influence dependencies, we propose a co-attention mechanism to capture the processes of diffusion cascades using positional embeddings. Given a set of activated seed users, we jointly exploit diffusion cascades and social links to predict which users will be influenced. We conduct extensive experiments on four real-world datasets. Empirical results demonstrate that the proposed H-Diffu model significantly outperforms several state-of-the-art diffusion prediction frameworks.
Keyword:
Integrated circuit modeling
Social networking (online)
Predictive models
Task analysis
Diffusion processes
Recurrent neural networks
Logic gates
Hyperbolic representations
social influence
diffusion prediction
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
引用论文
.png){kind=icon}
Cul4a as a New Interaction Protein of PARP1 Inhibits Oxidative Stress-Induced H9c2 Cell Apoptosis
OAAI
OAAI分享收藏Information Diffusion Prediction with Network Regularized Role-based User Representation Learning
PREAI
分享收藏
查看更多内容