Evaluating Loss Functions for Graph Neural Networks: Towards Pretraining and Generalization

TL;DR

This comprehensive study evaluates how different loss functions and GNN architectures interact across various tasks, revealing that hybrid losses and the GIN architecture often outperform others in real-world graph learning scenarios.

Contribution

It provides the first large-scale, systematic analysis of multiple GNN architectures combined with a wide range of loss functions across diverse datasets and settings.

Findings

Hybrid loss functions outperform single losses in robustness.

GIN architecture consistently achieves top performance with Cross-Entropy loss.

GAT with hybrid losses shows strong specialized performance.

Abstract

Graph Neural Networks (GNNs) became useful for learning on non-Euclidean data. However, their best performance depends on choosing the right model architecture and the training objective, also called the loss function. Researchers have studied these parts separately, but a large-scale evaluation has not looked at how GNN models and many loss functions work together across different tasks. To fix this, we ran a thorough study - it included seven well-known GNN architectures. We also used a large group of 30 single plus mixed loss functions. The study looked at both inductive and transductive settings. Our evaluation spanned three distinct real-world datasets, assessing performance in both inductive and transductive settings using 21 comprehensive evaluation metrics. From these extensive results (detailed in supplementary information 1 \& 2), we meticulously analyzed the top ten model-loss combinations for each metric based on their average rank. Our findings reveal that, especially for the inductive case: 1) Hybrid loss functions generally yield superior and more robust performance compared to single loss functions, indicating the benefit of multi-objective optimization. 2) The GIN architecture always showed the highest-level average performance, especially with Cross-Entropy loss. 3) Although some combinations had overall lower average ranks, models such as GAT, particularly with certain hybrid losses, demonstrated incredible specialized strengths, maximizing the most top-1 results among the individual metrics, emphasizing subtle strengths for particular task demands. 4) On the other hand, the MPNN architecture typically lagged behind the scenarios it was tested against.