  Graphs are structured data that models complex relations between real-world
entities. Heterophilous graphs, where linked nodes are prone to be with
different labels or dissimilar features, have recently attracted significant
attention and found many applications. Meanwhile, increasing efforts have been
made to advance learning from heterophilous graphs. Although there exist
surveys on the relevant topic, they focus on heterophilous GNNs, which are only
sub-topics of heterophilous graph learning. In this survey, we comprehensively
overview existing works on learning from graphs with heterophily.First, we
collect over 180 publications and introduce the development of this field.
Then, we systematically categorize existing methods based on a hierarchical
taxonomy including learning strategies, model architectures and practical
applications. Finally, we discuss the primary challenges of existing studies
and highlight promising avenues for future research.More publication details
and corresponding open-source codes can be accessed and will be continuously
updated at our
repositories:https://github.com/gongchenghua/Papers-Graphs-with-Heterophily.
