Authors: Florian Grötschla, Jiaqing Xie, Roger Wattenhofer
Abstract: Recent advances in Graph Neural Networks (GNNs) and Graph Transformers (GTs)
have been driven by innovations in architectures and Positional Encodings
(PEs), which are critical for augmenting node features and capturing graph
topology. PEs are essential for GTs, where topological information would
otherwise be lost without message-passing. However, PEs are often tested
alongside novel architectures, making it difficult to isolate their effect on
established models. To address this, we present a comprehensive benchmark of
PEs in a unified framework that includes both message-passing GNNs and GTs. We
also establish theoretical connections between MPNNs and GTs and introduce a
sparsified GRIT attention mechanism to examine the influence of global
connectivity. Our findings demonstrate that previously untested combinations of
GNN architectures and PEs can outperform existing methods and offer a more
comprehensive picture of the state-of-the-art. To support future research and
experimentation in our framework, we make the code publicly available.
Source: http://arxiv.org/abs/2411.12732v1
