- cora, citeseer and pubmed [46] are three networks that contain citations, with nodes representing documents and edges representing citation links. The features of the nodes are represented as bag-of-words, capturing the content of the documents. The goal is to predict the academic topic of each paper. We follow the semi-supervised settings in [23], using randomly sampled 20 instances per class as training set, 500 instances as validation set, and 1,000 instances as testing set.
- actor is the actor-only induced subgraph of the film-directoractor-writer network [49]. In this dataset, each node corresponds to an actor, and the edges between nodes signify their co-occurrence on the same Wikipedia page. The node features are comprised of keywords extracted from the respective actors' Wikipedia pages. The goal is to classify the nodes into five categories based on the content of the actors' Wikipedia pages. This dataset exhibits a relatively low homophily ratio and belongs to a heterophilic graph.
- squirrel and chameleon are two page-page networks that focus on on specific topics in Wikipedia [41]. In these datasets, the nodes represent web pages, and the edges denote mutual links between the pages. The node features are composed of a selection of informative nouns extracted from the corresponding Wikipedia pages. The goal is to categorize the nodes into five distinct groups based on the average monthly traffic received by each web page. Furthermore, these datasets are recognized as heterophilic graphs, indicating the presence of connections between nodes with distinct labels. A recent paper [38] indicates that the original split of these datasets introduces overlapping nodes between training and testing, and propose a new data split that filters out the overlapping nodes. We use its provided split for evaluation.
- deezer-europe is a user-user network of European members of the music streaming service Deezer [42]. In this network, the nodes represent users, while the edges signify mutual friendships between these users. The node features are based on the artists that the streamers have liked. The goal is to classify the gender of the users. We follow the benchmark setting in [32] and use a random  $50\% / 25\% / 25\%$  train-valid/test split.
- ogbn-proteins consists of a graph where nodes correspond to proteins and edges indicate various biologically significant associations between proteins, categorized by their types (based on species). Each edge is accompanied by 8-dimensional features, with each dimension representing the approximate confidence level of a specific association type and ranging from 0 to 1. The proteins in the dataset originate from 8 different species. The goal is to predict the presence or absence of 112 protein functions using a multi-label binary classification approach. We use the public OGB split [19].
- ognn-arxiv is a citation network among all Computer Science (CS) Arxiv papers, as described by [19]. In this network, each node corresponds to an Arxiv paper, and the edges indicate the citations between papers. Each paper is associated with a 128-dimensional feature vector, obtained by averaging the word embeddings of its title and abstract. The word embeddings are generated using the WORD2VEC model. The objective is to predict the subject areas of Arxiv CS papers, specifically targeting 40 distinct subject areas. We adopt the split of [19], which involves training the model on papers published until 2017, validating on those published in 2018, and testing on papers published from 2019 onwards.
- Amazon2M is derived from the Amazon Co-Purchasing network [33]. In this dataset, each node represents a product, and the presence of a graph link between two products indicates that they are frequently purchased together. The node features are generated by extracting bag-of-word features from the product descriptions. The labels assigned to the products are based on the top-level categories they belong to. We follow the recent work [57] using random split with the ratio  $50\% / 25\% / 25\%$ .
- pokec [26] is a large-scale social network dataset that encompasses various features, including profile information like geographical region, registration time, and age. The goal is to predict the gender of users based on these features. For data split, we randomly divide the nodes into training, validation, and testing sets, with ratios of  $10\%$ ,  $10\%$ , and  $80\%$ , respectively.
- ogbn-papers100M is a gigantic web-scale graph that consists of 111 million papers as nodes. The difference of this dataset from others is that not all nodes are labeled and the labeled ratio is relatively low. The labeled portion comprises approximately 1.5 million