Graph Attention Retrospective. (arXiv:2202.13060v4 [cs.LG] UPDATED)

Graph-based learning is a rapidly growing sub-field of machine learning with
applications in social networks, citation networks, and bioinformatics. One of
the most popular models is graph attention networks. They were introduced to
allow a node to aggregate information from features of neighbor nodes in a
non-uniform way, in contrast to simple graph convolution which does not
distinguish the neighbors of a node. In this paper, we study theoretically this
expected behaviour of graph attention networks. We prove multiple results …

arxiv attention graph retrospective