GJDNet proposes feature-driven soft structural disentanglement and a Spherical Decision Boundary to achieve robust node classification on graphs with varying assortativity against adversarial attacks.
Adversarial attacks on graph neural networks via meta learning
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
T2T-LA is an LLM agent that generates a useful graph topology in one shot from failed topologies and scores without feature access or task knowledge.
Supervised GNNs show higher baseline accuracy on community detection while unsupervised ones like DMoN prove more resilient to attribute shifts, edge deletions, and adversarial perturbations, with stronger communities reducing performance drops.
citing papers explorer
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GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
GJDNet proposes feature-driven soft structural disentanglement and a Spherical Decision Boundary to achieve robust node classification on graphs with varying assortativity against adversarial attacks.
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T2T-LA: A Topology-to-Topology LLM Agent for Graph Learning with Neither Feature Access nor Task Knowledge
T2T-LA is an LLM agent that generates a useful graph topology in one shot from failed topologies and scores without feature access or task knowledge.
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Community detection robustness of graph neural networks
Supervised GNNs show higher baseline accuracy on community detection while unsupervised ones like DMoN prove more resilient to attribute shifts, edge deletions, and adversarial perturbations, with stronger communities reducing performance drops.