EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Is heterophily a real nightmare for graph neural networks to do node classification?arXiv preprint arXiv:2109.05641
6 Pith papers cite this work, alongside 44 external citations. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 6representative citing papers
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
SIGMA integrates SimRank for one-time global similarity aggregation in heterophilous GNNs, achieving O(n) complexity and reported 5x speedup on large graphs with SOTA accuracy.
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
A survey proposing a systematic taxonomy of GNNs for heterophilic graphs along with analyses of their relations to other graph research domains.
A survey compiling graph rewiring techniques for mitigating over-squashing and over-smoothing in GNNs.
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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Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
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SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
SIGMA integrates SimRank for one-time global similarity aggregation in heterophilous GNNs, achieving O(n) complexity and reported 5x speedup on large graphs with SOTA accuracy.
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Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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Graph Neural Networks for Graphs with Heterophily: A Survey
A survey proposing a systematic taxonomy of GNNs for heterophilic graphs along with analyses of their relations to other graph research domains.
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Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
A survey compiling graph rewiring techniques for mitigating over-squashing and over-smoothing in GNNs.