EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Combining label propagation and simple models out-performs graph neural networks
3 Pith papers cite this work. Polarity classification is still indexing.
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Graph Cascades uses contagion diffusion to rewire graphs by promoting reinforced multi-hop node pairs to direct neighbors, improving GNN performance on heterophilic and moderate-degree homophilic graphs under specified conditions.
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.
citing papers explorer
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
Graph Cascades uses contagion diffusion to rewire graphs by promoting reinforced multi-hop node pairs to direct neighbors, improving GNN performance on heterophilic and moderate-degree homophilic graphs under specified conditions.
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AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.