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Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning

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arXiv:2606. 05046v1 Announce Type: cross Abstract: We introduce Graph Cascades, a mesoscopic rewiring strategy for Graph Neural Networks (GNNs) and Graph Transformers (GTs) that captures intermediate-scale graph structure beyond purely local edges or fully global attention.

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Authors: Meher Chaitanya, My Le, Luana Ruiz
Released: 2026-06-04 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: class, rect, under, rate, full. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.05046

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