Geometric instability of graph neural networks on large graphs

• URL: http://arxiv.org/abs/2308.10099v2
• Date: Tue, 28 Nov 2023 10:35:06 GMT
• Title: Geometric instability of graph neural networks on large graphs
• Authors: Emily Morris, Haotian Shen, Weiling Du, Muhammad Hamza Sajjad, Borun Shi
• Abstract summary: We analyse the geometric instability of embeddings produced by graph neural networks (GNNs) We propose a simple, efficient and graph-native Graph Gram Index (GGI) to measure such instability. This allows us to study the varying instability behaviour of GNN embeddings on large graphs for both node classification and link prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We analyse the geometric instability of embeddings produced by graph neural networks (GNNs). Existing methods are only applicable for small graphs and lack context in the graph domain. We propose a simple, efficient and graph-native Graph Gram Index (GGI) to measure such instability which is invariant to permutation, orthogonal transformation, translation and order of evaluation. This allows us to study the varying instability behaviour of GNN embeddings on large graphs for both node classification and link prediction.

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