ASTDP-GAD unifies spiking neural computation, STDP learning, and graph anomaly detection with claimed theoretical guarantees on encoding, convergence, and score calibration.
Label information enhanced fraud detection against low homophily in graphs
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.
citing papers explorer
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Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
ASTDP-GAD unifies spiking neural computation, STDP learning, and graph anomaly detection with claimed theoretical guarantees on encoding, convergence, and score calibration.
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AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
AdvSynGNN uses multi-resolution structural synthesis, contrastive objectives, an adaptive transformer, and an adversarial propagation engine with residual label correction to improve node-level predictions on challenging graph topologies.