# XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data
## 4 Experiment  
### Dataset  
Table 1 lists five real node regression datasets with different attributes, including four heterogeneous datasets and one isomorphic dataset. California Housing [24] provides housing information and demographics of a specific area in California in 1990, with 20,640 instances and 8 numerical features commonly used in regression problems to predict property prices. We retain the following node features: median, house age, average room size, average bedroom size, population, and average occupancy percentage. County [25] contains statistical information about US counties. A node represents a county, and if two nodes share the same edge, they form a connection. We retain the following node features commonly used in regression problems to predict unemployment: DEM, GOP, Median Income, MigraRate, BirthRate, DeathRate, and BachelorRate. VK [26] is a social network dataset, and in this paper, we use the open-access sub-sample of the VK social network of the top 1 million users. The following node features are retained: country, city, followers_count, has_mobile, last_seen_time, last_seen_platform, political, languages, religion_id, alcohol, smoking, relation, sex, and university, commonly used in regression problems to predict age. Wiki [27] is a page-page network isomorphic dataset on the topic of squirrels, with retained node features being bags of informative nouns (3148 in total) appearing in the primary text of Wikipedia articles, and the task is to predict the monthly average traffic per article between October 2017 and November 2018. Avazu [28] contains records of users clicking on advertisements in mobile advertising scenarios. The node features retained are the first 100,000 rows to calculate the click-through rate per device id, filtering out ids that do not have at least 10 advertisements displayed. The nodes are characterized by the anonymity categories: C1, C14, C15, C16, C17, C18, C19, C20, and C21. It is frequently used in regression problems to predict the click-through rate per device. Table 2 also lists five real-node classification datasets with different attributes. We chose the House and VK datasets from the regression task because there were not many publicly available datasets with nodes that had different properties. We then made two new datasets, House_class and VK_class, by breaking the target labels into separate classes. We also selected two sparse node classification datasets, SLAP and DBLP. SLAP [29] is constructed as a multi-hub network that connects different types of nodes through relational edges between nodes, which include a variety of associations such as gene-gene, gene-disease, and disease-compound. Regression problems commonly use it to predict which of the 15 gene types it belongs to. DBLP [30] is a multi-relational academic network dataset that includes node types such as author, paper, and conference, as well as edge relationships such as author-paper, paper-conference, and author-author. We perform various graph analysis tasks, such as node classification, link prediction, and community detection. Finally, we select an isomorphic dataset, OGB-ArXiv [31], in which each node represents a paper, and the edges signify citation relations, i.e., citations between papers. This dataset has 169,343 nodes, 1,166,243 edges, and 40 categories, making it a popular choice for classifying papers into domain categories. We preprocess the data by normalizing numerical features with a zero mean and unit variance, coding categorical features with ordinal coding, and supplementing missing values with zeros.