BOCLOAK uses optimal transport on spatio-temporal features to create sparse, constraint-aware attacks that raise success rates up to 80% against GNN bot detectors while slashing memory use.
10 Table 5.Summary of notations used in this paper
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A large-scale standardized benchmark of GNN attacks and defenses reveals that target node selection and attacked-model training process can completely distort measured attack effectiveness.
ASPECT learns per-node spectral fusion policies in graph contrastive learning, regularized by channel-wise contrastive evidence, to outperform uniform fusion on homophilic and heterophilic benchmarks.
Modeling recommender systems as control systems shows that time-optimized fairness interventions can improve overall long-term performance rather than merely trading off against utility.
citing papers explorer
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Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
BOCLOAK uses optimal transport on spatio-temporal features to create sparse, constraint-aware attacks that raise success rates up to 80% against GNN bot detectors while slashing memory use.
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Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
A large-scale standardized benchmark of GNN attacks and defenses reveals that target node selection and attacked-model training process can completely distort measured attack effectiveness.
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ASPECT: Node-Level Adaptive Spectral Fusion for Graph Contrastive Learning
ASPECT learns per-node spectral fusion policies in graph contrastive learning, regularized by channel-wise contrastive evidence, to outperform uniform fusion on homophilic and heterophilic benchmarks.
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Recommender Systems as Control Systems
Modeling recommender systems as control systems shows that time-optimized fairness interventions can improve overall long-term performance rather than merely trading off against utility.