Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network

Yifei Sun, Haoran Deng, Yang Yang, Chunping Wang, Jiarong Xu, Renhong Huang, Linfeng Cao, Yang Wang, Lei CHEN

Abstract:

Graph neural networks (GNNs) have been intensively studied in various real-world tasks. However, the homophily assumption of GNNs’ aggregation function limits their representation learning
ability in heterophily graphs. In this paper, we shed
light on the path level patterns in graphs that can explicitly reflect rich semantic and structural information. We therefore propose a novel Structure-aware
Path Aggregation Graph Neural Network (PathNet)
aiming to generalize GNNs for both homophily
and heterophily graphs. Specifically, we first introduce a maximal entropy path sampler, which
helps us sample a number of paths containing structural context. Then, we introduce a structure-aware
recurrent cell consisting of order-preserving and
distance-aware components to learn the semantic
information of neighborhoods. Finally, we model
the preference of different paths to target node after
path encoding. Experimental results demonstrate
that our model obtains significant improvements
in node classification on both heterophily and homophily graphs.