NeighborDiv detects graph anomalies via variance of inter-neighbor feature similarities under a new Neighbor-to-Neighbor Diversity Paradigm, achieving SOTA results with zero volatility in zero-shot cross-domain settings.
Collective classification in network data.AI magazine, 29(3):93–93, 2008
3 Pith papers cite this work. Polarity classification is still indexing.
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A matrix shuffling mechanism for edge-differentially private spectral clustering achieves Õ(1/n) misclassification error via privacy amplification and a unified Davis-Kahan plus margin analysis, outperforming Analyze Gauss and noisy power iteration.
InGSL reduces edge redundancy in existing graph structure learning methods by adding a mutual-information-guided diversity term, delivering better results with fewer edges across six tested frameworks.
citing papers explorer
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NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity
NeighborDiv detects graph anomalies via variance of inter-neighbor feature similarities under a new Neighbor-to-Neighbor Diversity Paradigm, achieving SOTA results with zero volatility in zero-shot cross-domain settings.
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Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency
A matrix shuffling mechanism for edge-differentially private spectral clustering achieves Õ(1/n) misclassification error via privacy amplification and a unified Davis-Kahan plus margin analysis, outperforming Analyze Gauss and noisy power iteration.
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Informative Graph Structure Learning
InGSL reduces edge redundancy in existing graph structure learning methods by adding a mutual-information-guided diversity term, delivering better results with fewer edges across six tested frameworks.