Topology Matters: A Cautionary Case Study of Graph SSL on Neuro-Inspired Benchmarks

• URL: http://arxiv.org/abs/2602.03217v1
• Date: Tue, 03 Feb 2026 07:35:54 GMT
• Title: Topology Matters: A Cautionary Case Study of Graph SSL on Neuro-Inspired Benchmarks
• Authors: May Kristine Jonson Carlon, Su Myat Noe, Haojiong Wang, Yasuo Kuniyoshi,
• Abstract summary: We introduce a hierarchical self-supervised learning (SSL) framework that jointly learns node-, edge-, and graph-level embeddings.<n>We construct a controllable synthetic benchmark mimicking the topological properties of connectomes.<n>Our results expose a fundamental pitfall in applying generic graph SSL to connectome-like data.
• Score: 3.9042998611300455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding how local interactions give rise to global brain organization requires models that can represent information across multiple scales. We introduce a hierarchical self-supervised learning (SSL) framework that jointly learns node-, edge-, and graph-level embeddings, inspired by multimodal neuroimaging. We construct a controllable synthetic benchmark mimicking the topological properties of connectomes. Our four-stage evaluation protocol reveals a critical failure: the invariance-based SSL model is fundamentally misaligned with the benchmark's topological properties and is catastrophically outperformed by classical, topology-aware heuristics. Ablations confirm an objective mismatch: SSL objectives designed to be invariant to topological perturbations learn to ignore the very community structure that classical methods exploit. Our results expose a fundamental pitfall in applying generic graph SSL to connectome-like data. We present this framework as a cautionary case study, highlighting the need for new, topology-aware SSL objectives for neuro-AI research that explicitly reward the preservation of structure (e.g., modularity or motifs).

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