Heterophilous graph datasets

A graph dataset is called heterophilous if nodes prefer to connect to other nodes that are not similar to them. For example, in financial transaction networks, fraudsters often perform transactions with non-fraudulent users, and in dating networks, most connections are between people of opposite genders. Learning under heterophily is an important subfield of graph ML. Thus, having diverse and reliable benchmarks is essential.

We propose a benchmark of five diverse heterophilous graphs that come from different domains and exhibit a variety of structural properties. Our benchmark includes a word dependency graph Roman-empire, a product co-purchasing network Amazon-ratings, a synthetic graph emulating the minesweeper game Minesweeper, a crowdsourcing platform worker network Tolokers, and a question-answering website interaction network Questions.