iFiG: Individually Fair Multi-view Graph Clustering

Yian Wang, Jian Kang, Yinglong Xia, Jiebo Luo, Hanghang Tong

Abstract: A multi-view graph comprises multiple single-view graphs with the same set of nodes but different types of edges. In many real-world applications, graphs are often collected from multiple sources, forming multi-view graphs. For example, users could have accounts on numerous social platforms like Facebook and Twitter; the infrastructure network of cities exhibits different topologies considering different types of infrastructures (e.g., power grid, road network). Up to now, researchers have proposed a variety of multi-view graph mining models, including clustering [1] , embedding [2] , and graph neural networks [3] .