Representations of Graphs and Their Impact on Graph Neural Networks - Möller,Tobias (Master's Theses in Mathematical Sciences, 2025)
Graph Neural Networks (GNNs) are increasingly used for predictive tasks on data where relationships between entities are best represented as graphs. Much work has been done to improve GNN architectures, but how the ac- tual data graph should be designed is not well studied. In this Master’s Thesis, we investigate how different graph representations, specifically node classification and link prediction representations, impact the performance of GNN models (GraphSAGE, GAT, HAN) across datasets with varying characteristics (label homophily, hetero- geneity, and multi-labeled datasets). To ensure a fair comparison, models for both representations are designed with similar architectures and evaluated using identical data splits. Experiments using common node classifica- tion benchmark datasets show that the node classification representation is preferred in most cases. However, link prediction representations show advantages on graphs with high homophily and lower node degrees. This study provides practical guidelines for selecting suitable graph representations for predictive tasks on graphs, and suggests directions for future research on the topic.
Keywords: Graph Machine Learning; Graph Neural Networks; GNN; Graph Representation Learning; Graph Representations; Link Prediction; Node Classification; Graph Attention Network; Heterogeneous Graphs; Homogeneous Graphs; Neo4j; Graph Database