@inproceedings{98d7d39cb8ca428d86e4f566e7b8a3fe,
title = "Predicting Information Diffusion Cascades Using Graph Attention Networks",
abstract = "Effective information cascade prediction plays a very important role in suppressing the spread of rumors in social networks and providing accurate social recommendations on social platforms. This paper improves existing models and proposes an end-to-end deep learning method called CasGAT. The method of graph attention network is designed to optimize the processing of large networks. After that, we only need to pay attention to the characteristics of neighbor nodes. Our approach greatly reduces the processing complexity of the model. We use realistic datasets to demonstrate the effectiveness of the model and compare the improved model with three baselines. Extensive results demonstrate that our model outperformed the three baselines in the prediction accuracy.",
keywords = "Graph attention network, Information cascade prediction, Social network",
author = "Meng Wang and Kan Li",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 27th International Conference on Neural Information Processing, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",
year = "2020",
doi = "10.1007/978-3-030-63820-7_12",
language = "English",
isbn = "9783030638191",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "104--112",
editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, {Andrew Chi-Sing} and Kwok, {James T.} and Chan, {Jonathan H.} and Irwin King",
booktitle = "Neural Information Processing - 27th International Conference, ICONIP 2020, Proceedings",
address = "Germany",
}