PO-GNN: Position-observant inductive graph neural networks for position-based prediction

Abstract

Most graph neural networks (GNNs) map local neighborhood information into node embeddings but lack positional information. It results in similar node embeddings for nodes in similar local neighborhoods, leading to poor performance. We propose a position-observant inductive graph neural network (PO-GNN) architecture that captures the global positions of the nodes using the proposed novel closeness-distance metric. We demonstrate that the problem of anchor node selection, a significant component of position-aware GNNs, is NP-hard. Additionally, we utilize a heuristic anchor selection approach to obtain equitably distributed and asymmetric reference nodes over the given graph. In particular, PO-GNN significantly achieved a 50% relative improvement in the AUC metric for the Link Prediction (LP) over five different datasets and 85% for the Pairwise Node Classification (PNC) tasks over four different datasets and compared to the common GNN architectures and 1.805% and 15.29% improvement over state-of-the-art position-aware GNNs in LP and PNC tasks respectively. Furthermore, we also perform ablation studies over the length and number of random walks along with five aggregation methods and four update methods.