A Algorithm

Each dataset contains one citation network, where nodes mean papers and edges mean citation relationships. We use the public split for linear evaluation, where each class has fixed 20 nodes for training, another fixed 500 nodes and 1000 nodes are for validation/test respectively. Coauther CS, Coauther Physics are co-authorship graphs based on the Microsoft Academic Graph from the KDD Cup 2016 challenge [35]. Nodes are authors, that are connected by an edge if they co-authored a paper; node features represent paper keywords for each author's papers, and class labels indicate most active fields of study for each author. As there is no public split for these datasets, we randomly split the nodes into train/validation/test (10%/10%/80%) sets. Amazon Computer, Amazon Photo are segments of the Amazon co-purchase graph [26], where nodes represent goods, edges indicate that two goods are frequently bought together; node features are bag-of-words encoded product reviews, and class labels are given by the product category. We also use a 10%/10%/80% split for these two datasets. For all datasets, we use the processed version provided by Deep Graph Library [49]