.png){kind=icon}
.png){kind=icon}
Return
Predicting information diffusion via deep temporal convolutional networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.is.2022.102045.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Information cascade diffusion is ubiquitous in modern social medias and other fields, such as viral marketing, paper citation dynamics, and public opinion communication. However, the existing deep learning-based methods to model and predict the growth of information cascades pay much attention to the nodes in the cascades and ignore the overall propagation structure of the cascades. Simultaneously, they are usually of high complexity and low computational efficiency. In this paper, we propose a novel deep learning framework for information cascade predictor, named CasTCN, which can effectively capture the structure dynamic of the information cascades. Firstly, we design a dynamic mapping mechanism, which can represent the overall structure of information cascades an its dynamic evolution. Secondly, we extract the higher-level representation of cascade network through deep temporal convolutional network. Finally, the prediction module is applied to predict the growth scale of information cascades. Since CasTCN is a graph-level method, it has relatively fewer model parameters, so the model training time is also less than other baseline methods. Our experiments on two real world datasets show that CasTCN can achieve better performance than other baseline methods on both effectiveness and efficiency. Compared with the state-of-the-art methods, the prediction error is reduced by 8.58%, 7.13% and 6.89% respectively on 3 subdatasets of Weibo, and 15.00%, 11.02% and 9.12% on 3 subdatasets of APS respectively. Also, the training time is much more less than baselines. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Social networks
Information diffusion
Dynamic mapping
Temporal convolutional networks
Journal
IF:
3.9
Papers:
2.8K
Citations:
1.8K