Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

• URL: http://arxiv.org/abs/2607.03587v1
• Date: Fri, 03 Jul 2026 20:11:34 GMT
• Title: Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
• Abstract summary: NetinfoGC is a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.<n>We introduce a training-free NUI estimation procedure based on clustering consistency with ground-truth labels.<n>We observe a strong correlation between estimated NUI and downstream classification accuracy, validating NUI as an effective measure of representation utility.
• Score: 0.4910937238451485
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning. Unlike conventional graph neural network approaches that rely on end-to-end training of black-box embeddings, NetinfoGC constructs a family of permutation-invariant graph representations derived from propagation-based mechanisms and classical structural descriptors, including graph centrality measures. To evaluate representation quality, we introduce a training-free NUI estimation procedure based on clustering consistency with ground-truth labels, providing a proxy for task-relevant information without supervised learning. We further exploit the same representations using sparse-group LASSO regularization, enabling automatic selection of informative structural descriptors while suppressing redundant ones. Experiments on benchmark datasets show that classical centrality measures are highly competitive with learned propagation-based representations, and in several cases yield superior performance. Moreover, we observe a strong correlation between estimated NUI and downstream classification accuracy, validating NUI as an effective measure of representation utility. Overall, NetinfoGC provides a unified and interpretable framework for evaluating and exploiting graph representations without requiring end-to-end neural training.

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