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GNN-Retro: Retrosynthetic Planning with Graph Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2022Retrosynthetic planning plays an important role in the field of organic chemistry, which could generate a synthetic route for the target product. The synthetic route is a series of reactions which are started from the available molecules. The most challenging problem in the generation of the synthetic route is the large search space of the candidate ...
Peng Han 0005 +7 more
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OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
IEEE Transactions on Knowledge and Data Engineering, 2023 19 ...
Haoyang Li, Xin Wang, Ziwei Zhang
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Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture heterogeneous structures and attributes of an underlying graph. Furthermore, though many Heterogeneous GNN (HGNN) variants have been proposed and have achieved state-of-the-art ...
Qingqing Long +3 more
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While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture heterogeneous structures and attributes of an underlying graph. Furthermore, though many Heterogeneous GNN (HGNN) variants have been proposed and have achieved state-of-the-art ...
Qingqing Long +3 more
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Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
The prevalence of graph structures in real-world scenarios enables important tasks such as node classification and link prediction. Graphs in many domains follow a long-tailed distribution in their node degrees, i.e., a significant fraction of nodes are tail nodes with a small degree. Although recent graph neural networks (GNNs) can learn powerful node
Zemin Liu +2 more
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The prevalence of graph structures in real-world scenarios enables important tasks such as node classification and link prediction. Graphs in many domains follow a long-tailed distribution in their node degrees, i.e., a significant fraction of nodes are tail nodes with a small degree. Although recent graph neural networks (GNNs) can learn powerful node
Zemin Liu +2 more
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GPL-GNN: Graph prompt learning for graph neural network
Knowledge-Based SystemsYing Wang
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BA-GNN: On Learning Bias-Aware Graph Neural Network
2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022Zhengyu Chen 0001, Teng Xiao, Kun Kuang
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A-GNN: Anchors-Aware Graph Neural Networks for Node Embedding
2020With the rapid development of information technology, it has become increasingly popular to handle and analyze complex relationships of various information network applications, such as social networks and biological networks. An unsolved primary challenge is to find a way to represent the network structure to efficiently compute, process and analyze ...
Chao Liu 0007 +6 more
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GNN-MgrPool: Enhanced graph neural networks with multi-granularity pooling for graph classification
Information Sciences, 2023Graph neural networks (GNNs) have gained sufficient attention and are applied to various domain tasks. At present, numerous pooling approaches are being proposed to aggregate node features and obtain node embeddings. However, current GNNs are black-box models that typically use a flat or single pooling step to aggregate nodes, which only considers the ...
Haichao Sun +3 more
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