Back to Search
Start Over
Identify influential nodes in social networks with graph multi-head attention regression model.
- Source :
-
Neurocomputing . Apr2023, Vol. 530, p23-36. 14p. - Publication Year :
- 2023
-
Abstract
- Identifying influential nodes in social networks is a fundamental task. Due to the development of Graph Neural Networks, Graph Convolution Network (GCN) based model has been introduced to solve this problem. Compared to traditional methods, the existing GCN-based models are more accurate in identifying influential nodes because they can better aggregate the multi-dimension features. However, the GCN-based method treats this problem as a binary classification task rather than a regression task, making it less practical. To make the GCN-based model more practical, we treat identifying influential nodes as a regression task. Moreover, when aggregating neighbor features, GCN ignores the difference in neighbor importance, which will affect the prediction performance of the GCN-based models. This paper proposes a graph multi-head attention regression model to address these problems. Vast experiments on twelve real-world social networks demonstrate that the proposed model significantly outperforms baseline methods. To the best of our knowledge, this is the first work to introduce the multi-head attention mechanism to identify influential nodes in social networks. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SOCIAL networks
*REGRESSION analysis
*SOCIAL network analysis
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 530
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 162130844
- Full Text :
- https://doi.org/10.1016/j.neucom.2023.01.078