arXiv:2505.13087v2 Announce Type: replace-cross Abstract: We propose a novel benchmarking methodology for graph neural networks (GNNs) based on the graph alignment problem, a combinatorial optimization task that generalizes graph isomorphism by aligning two unlabeled graphs to maximize overlapping edges. We frame this problem as a self-supervised learning task and present several methods to generate graph alignment datasets using synthetic random graphs and real-world graph datasets from multiple domains. For a given graph dataset, we generate a family of graph alignment datasets with increasi
This paper proposes a novel benchmarking and self-supervised learning methodology for Graph Neural Networks, a critical area for improving AI's ability to understand complex relationships.
Improved GNN benchmarking can accelerate research and development in areas requiring robust relational AI, leading to more capable AI systems across various applications.
The proposed graph alignment method offers a new standard for evaluating and training GNNs, potentially leading to more accurate and generalizable models.
- · AI researchers
- · Machine learning developers
- · Sectors using complex networked data
- · AI models relying on less robust GNN architectures
More efficient development of advanced Graph Neural Networks.
Accelerated deployment of GNNs in areas like drug discovery, social network analysis, and recommendation systems.
Enhanced AI capabilities to model and predict complex real-world systems, leading to breakthroughs in scientific discovery and industrial optimization.
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