Publikation
Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis
Zhifei Li; Gerrit Großmann; Verena Wolf
In: Hocine Cherifi; Murat Donduran; Luis M. Rocha; Chantal Cherifi; Onur Varol. International Conference on Complex Networks and Their Applications. Pages 183-194, Springer Nature Switzerland, 2024.
Zusammenfassung
In recent years, a wide variety of graph neural network (GNN)
architectures have emerged, each with its own strengths, weaknesses, and
complexities. Various techniques, including rewiring, lifting, and node
annotation with centrality values, have been employed as pre-processing
steps to enhance GNN performance. However, there are no universally accepted best practices, and the impact of architecture and pre-processing
on performance often remains opaque.
This study systematically explores the impact of various graph transformations as pre-processing steps on the performance of common GNN
architectures across standard datasets. The models are evaluated based
on their ability to distinguish non-isomorphic graphs, referred to as expressivity.
Our findings reveal that certain transformations, particularly those augmenting node features with centrality measures, consistently improve
expressivity. However, these gains come with trade-offs, as methods like
graph encoding, while enhancing expressivity, introduce numerical inaccuracies widely-used python packages. Additionally, we observe that
these pre-processing techniques are limited when addressing complex
tasks involving 3-WL and 4-WL indistinguishable graphs.