Rethinking Graph Super-resolution: Dual Frameworks for Topological Fidelity

• URL: http://arxiv.org/abs/2511.08853v1
• Date: Thu, 13 Nov 2025 01:12:18 GMT
• Title: Rethinking Graph Super-resolution: Dual Frameworks for Topological Fidelity
• Authors: Pragya Singh, Islem Rekik,
• Abstract summary: Bipartite graph connecting LR and HR nodes enables structure-aware node super-resolution.<n> DEFEND learns edge representations by mapping HR edges to nodes of a dual graph, allowing edge inference via standard node-based GNNs.
• Score: 7.62013761499722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph super-resolution, the task of inferring high-resolution (HR) graphs from low-resolution (LR) counterparts, is an underexplored yet crucial research direction that circumvents the need for costly data acquisition. This makes it especially desirable for resource-constrained fields such as the medical domain. While recent GNN-based approaches show promise, they suffer from two key limitations: (1) matrix-based node super-resolution that disregards graph structure and lacks permutation invariance; and (2) reliance on node representations to infer edge weights, which limits scalability and expressivity. In this work, we propose two GNN-agnostic frameworks to address these issues. First, Bi-SR introduces a bipartite graph connecting LR and HR nodes to enable structure-aware node super-resolution that preserves topology and permutation invariance. Second, DEFEND learns edge representations by mapping HR edges to nodes of a dual graph, allowing edge inference via standard node-based GNNs. We evaluate both frameworks on a real-world brain connectome dataset, where they achieve state-of-the-art performance across seven topological measures. To support generalization, we introduce twelve new simulated datasets that capture diverse topologies and LR-HR relationships. These enable comprehensive benchmarking of graph super-resolution methods.

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