Name
Abstract | ||
|---|---|---|
Graph Neural Networks (GNNs), which extend deep neural networks to graph-structured data, have attracted increasing attention. They have been proven to be powerful for numerous graph related tasks such as graph classification, link prediction, and node classification. To adapt GNNs to graph classification, recent works aim to learn graph-level representation through a hierarchical pooling procedure. One major direction is to select important nodes to hierarchically coarsen the input graph and gradually reduce the information into the graph representation. However, most of the existing methods only select important nodes, which can be redundant and cannot represent the original graph well. Meanwhile, the information of non-selected nodes is often overlooked when generating a new coarser graph, which may lead to the tremendous loss of important structural and node feature information. In this paper, we propose a novel pooling operator RepPool to learn hierarchical graph representations. Specifically, we introduce the concept of representativeness that is combined with the importance for node selection and we provide a learnable way to integrate non-selected nodes. By combining the RepPool operator with conventional GCN convolutional layers, a hierarchical graph classification architecture is developed. Extensive experiments on various public benchmarks have demonstrated the effectiveness of the proposed method. The implementation of the proposed framework is available
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https://github.com/Juanhui28/RepPool/tree/master/RepPool. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICDM50108.2020.00039 | 2020 IEEE International Conference on Data Mining (ICDM) |
Keywords | DocType | ISSN |
n/a | Conference | 1550-4786 |
ISBN | Citations | PageRank |
978-1-7281-8317-6 | 1 | 0.35 |
References | Authors | |
34 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan-Hui Li | 1 | 20 | 2.25 |
| Ma Yao | 2 | 48 | 3.48 |
| Yiqi Wang | 3 | 31 | 3.77 |
| Charu C. Aggarwal | 4 | 9 | 3.20 |
| Chang-Dong Wang | 5 | 60 | 9.09 |
| Jiliang Tang | 6 | 3323 | 140.81 |