Hierarchical Graph Pooling With Self-Adaptive Cluster Aggregation
| العنوان: | Hierarchical Graph Pooling With Self-Adaptive Cluster Aggregation |
|---|---|
| المؤلفون: | Hai-Long Su, Xuesong Jiang, De-Shuang Huang, Xiao-Bo Zhu, Valeriya Gribova, Filaretov Vladimir F, Xiumei Wei, Zhi-Peng Li |
| المصدر: | IEEE Transactions on Cognitive and Developmental Systems. 14:1198-1207 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Structure (mathematical logic), Computer science, Node (networking), Pooling, Recommender system, computer.software_genre, Artificial Intelligence, Feature (machine learning), Graph (abstract data type), Differentiable function, Data mining, computer, Software, Selection (genetic algorithm) |
| الوصف: | Graph Neural Network (GNN) introduces deep neural networks into graph structure data. It has achieved advanced performance in many fields such as traffic prediction, recommendation systems, and computer vision, which has received extensive attention from the academic community. Most of the existing research on graph neural networks focuses on graph convolution, while graph pooling is usually ignored. Although there are also some graph pooling methods, most of the current pooling methods are based on top-k node selection. In the top-kbased pooling method, unselected nodes will be directly discarded, which will cause the loss of feature information during the pooling process. In this paper, we propose a novel graph pooling operator, called Hierarchical Graph Pooling with Self-Adaptive Cluster Aggregation (HGP-SACA), which uses a sparse and differentiable method to capture the graph structure. Before using top-k for cluster selection, the unselected clusters and the selected clusters in the neighbor perform an n-hop feature information aggregation. The merged clusters which contain neighborhood clusters are used for top-k selection, which can enhance the function of the unselected clusters. Through extensive theoretical analysis and experimental verification on multiple datasets, our experimental results show that combining the existing GNN architecture with HGP-SACA can achieve state-of-the-art results on multiple graph classification benchmarks, which proves the effectiveness of our proposed model. |
| تدمد: | 2379-8939 2379-8920 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::8e20e99aa6280c6623f6d68cb8437c0c https://doi.org/10.1109/tcds.2021.3100883 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........8e20e99aa6280c6623f6d68cb8437c0c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23798939 23798920 |
|---|