A neural networks using individual node features propagated via GPR
GPRGNN
This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network.
Hidden state feature extraction is performed by a neural networks using individual node features propagated via GPR. Note that both the GPR weights and parameter set
of the neural network are learned simultaneously in an end-to-end fashion (as indicated in red).
The learnt GPR weights of the GPR-GNN on real world datasets. Cora is homophilic while Texas is heterophilic (Here, H stands for the level of homophily defined in the main text, Equation (1)). An interesting trend may be observed: For the heterophilic case the weights alternate from positive to negative with dampening amplitudes. The shaded region corresponds to a 95% confidence interval.