Graph-level Neural Networks: Current Progress and Future Directions

• URL: http://arxiv.org/abs/2205.15555v1
• Date: Tue, 31 May 2022 06:16:55 GMT
• Title: Graph-level Neural Networks: Current Progress and Future Directions
• Authors: Ge Zhang, Jia Wu, Jian Yang, Shan Xue, Wenbin Hu, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu Aggarwal
• Abstract summary: Graph-level Neural Networks (GLNNs, deep learning-based graph-level learning methods) have been attractive due to their superiority in modeling high-dimensional data. We propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
• Score: 61.08696673768116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-level learning methods used to be the mainstream. However, with the increasing scale and complexity of graphs, Graph-level Neural Networks (GLNNs, deep learning-based graph-level learning methods) have been attractive due to their superiority in modeling high-dimensional data. Thus, a survey on GLNNs is necessary. To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling. The representative and state-of-the-art models in each category are focused on this survey. We also investigate the reproducibility, benchmarks, and new graph datasets of GLNNs. Finally, we conclude future directions to further push forward GLNNs. The repository of this survey is available at https://github.com/GeZhangMQ/Awesome-Graph-level-Neural-Networks.

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