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

2026-02-03

AI Summary

This work addresses the poor performance of generic graph self-supervised learning (SSL) methods on data exhibiting brain connectome-like topological properties, which stems from a fundamental mismatch between their objective functions and key neuroanatomical characteristics such as modularity. To bridge this gap, we propose a hierarchical self-supervised framework inspired by multimodal neuroimaging, which explicitly preserves topological structure through joint node-, edge-, and graph-level embeddings. We introduce the first controllable synthetic connectome-like graph benchmark and design a four-stage evaluation protocol. Experimental results demonstrate that existing invariance-based SSL approaches significantly underperform traditional topology-aware heuristics due to their neglect of community structure, thereby underscoring the necessity of structure-aware SSL objectives for neurotopologically informed representation learning.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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).

Problem

• graph self-supervised learning
• topology
• connectome
• community structure
• neuro-AI

Innovation

• topology-aware SSL
• hierarchical self-supervised learning
• connectome benchmark
• graph representation learning
• community structure preservation