\HGNN: Modelling Long-distance Node Relations for Improving General Graph Neural Networks
Table 2: Node classification results in comparison with recent state-of-the-art methods with standard dataset splitting Yang et al. (2016). We run 10 turns for each experiment and report the mean value and the standard deviation over the running times. [*] means the results are taken from the corresponding papers. Our set the new SOTA for the CORA and CiteSeer datasets.
['[BOLD] Method GCN*\xa0(Kipf and Welling,  2017 )', '[BOLD] CORA 81.5', '[BOLD] CORA', '[BOLD] PubMed 79.0', '[BOLD] PubMed', '[BOLD] CiteSeer 70.3', '[BOLD] CiteSeer']
In this subsection, we display the comparison results between our \HGNNand state-of-the-art node classification methods in the setting of standard Planetoid CiteSeer/PubMed datasets. For baselines, we choose classical GCN and GAT and some recent advanced methods. We can observe that our \HGNNoutperforms all the baselines and achieves state-of-the-art performance on CORA and CiteSeer. Our approach also shows competitive results on PubMed. The limited improvements on PubMed is because that there are only few labeled nodes (60) in the training set that provide inadequate supervision information for the node pair classification task (node pairs of the same category constitute a small part of all the node pairs, so there are few positive samples).