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.
Zero-shot generalist graph anomaly detection with unified neighborhood prompts
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PromptDyG performs unsupervised test-time prompt adaptation on frozen dynamic graph models via entropy minimization to guarantee larger positive-negative pair margins and improve online predictions.
ReFi-GAD uses a semantics-aware relational fingerprint and transformer-based model with SNR refinement to align heterogeneous features for generalist graph anomaly detection across unseen graphs.
RTTAD improves unsupervised tabular anomaly detection by combining collaborative dual-task learning during training with selective, risk-aware test-time contrastive learning that avoids anomaly contamination.
N2NSC framework detects anomalies in text-attributed graphs by enforcing node-to-neighborhood semantic consistency via two complementary fusion paths that align textual semantics with topology.
TEMG-TTA combines temporal motif-aware graph learning with test-time adaptation to improve OOD anomaly detection on blockchain graphs, reporting an average 54.88% gain over prior GAD methods on five real-world datasets.
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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PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
PromptDyG performs unsupervised test-time prompt adaptation on frozen dynamic graph models via entropy minimization to guarantee larger positive-negative pair margins and improve online predictions.
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Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
ReFi-GAD uses a semantics-aware relational fingerprint and transformer-based model with SNR refinement to align heterogeneous features for generalist graph anomaly detection across unseen graphs.
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When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
RTTAD improves unsupervised tabular anomaly detection by combining collaborative dual-task learning during training with selective, risk-aware test-time contrastive learning that avoids anomaly contamination.
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Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
N2NSC framework detects anomalies in text-attributed graphs by enforcing node-to-neighborhood semantic consistency via two complementary fusion paths that align textual semantics with topology.
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Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
TEMG-TTA combines temporal motif-aware graph learning with test-time adaptation to improve OOD anomaly detection on blockchain graphs, reporting an average 54.88% gain over prior GAD methods on five real-world datasets.