GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
Supervised commu- nity detection with line graph neural networks
3 Pith papers cite this work, alongside 120 external citations. Polarity classification is still indexing.
3
Pith papers citing it
120
external citations · external index
verdicts
UNVERDICTED 3representative citing papers
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.
GLSTaGAT is a spatial-temporal graph attention network using data-driven fusion graphs, global-local blocks, node normalization, and a transformer encoder to outperform baselines on real-world network traffic datasets.
citing papers explorer
-
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.
-
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.
-
Global-local Spatial-temporal Aware Graph Attention Network for Network Traffic Forecasting
GLSTaGAT is a spatial-temporal graph attention network using data-driven fusion graphs, global-local blocks, node normalization, and a transformer encoder to outperform baselines on real-world network traffic datasets.