Research Article
CPNSA: Cascade Prediction with Network Structure Attention
@INPROCEEDINGS{10.1007/978-3-030-67537-0_5, author={Chaochao Liu and Wenjun Wang and Pengfei Jiao and Yueheng Sun and Xiaoming Li and Xue Chen}, title={CPNSA: Cascade Prediction with Network Structure Attention}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Cascade prediction Deep learning Network structure influences Recurrent neural network Cascade behavior}, doi={10.1007/978-3-030-67537-0_5} }- Chaochao Liu
Wenjun Wang
Pengfei Jiao
Yueheng Sun
Xiaoming Li
Xue Chen
Year: 2021
CPNSA: Cascade Prediction with Network Structure Attention
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_5
Abstract
Online social medias provide convenient platforms for information spread, which makes the social network structure plays important role on online information spread. Although online social network structure can be obtained easily, few researches use network structure information in the cascade of the resharing prediction task. In this paper, we propose a cascade prediction method (named by CPNSA) involves the network structure information into cascade prediction of resharing task. The method is based on the recurrent neural network, and we introduce a network structure attention to incorporates the network structure information into cascade representation. In order to fuse network structure information with cascading time series data, we use network embedding method to get the representations of nodes from the network structure firstly. Then we use the attention mechanism to capture the structural dependency for cascade prediction of resharing. Experiments are conducted on both synthetic and real-world datasets, and the results show that our approach can effectively improve the performance of the cascade prediction of resharing.
