astpd-gad-neuromorphic-graph-anomaly

name	astpd-gad-neuromorphic-graph-anomaly
description	ASTDP-GAD: Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks. Integrates adaptive spiking temporal dynamics plasticity with graph anomaly detection for energy-efficient neuromorphic deployment.

ASTDP-GAD: Adaptive STDP Graph Anomaly Detection

Source: arXiv:2605.13863 (May 15, 2026) Authors: Abdul Joseph Fofanah, Lian Wen, David Chen, Tsungcheng Yao, Kwabena Sarpong Categories: cs.NE, cs.LG

Problem Statement

Anomaly detection in dynamic networks is critical for cybersecurity, industrial monitoring, and other applications. Existing methods face challenges in:

• Energy efficiency - especially for continuous monitoring
• Temporal precision - capturing time-varying graph patterns
• Adaptability - handling evolving network structures

Key Innovations

1. Temporal Spike Graph Encoding with Adaptive LIF

• Encodes dynamic graph data as temporal spike trains
• Adaptive Leaky Integrate-and-Fire (LIF) neuron dynamics
• Information preservation with resolution scaling linearly in simulation steps

2. LIF-based Graph Attention (LIFGAT)

• Graph attention mechanism using LIF neurons
• Lateral inhibition for competitive attention
• Theoretical guarantee: approximates any continuous attention function
• Event-driven computation eliminates unnecessary processing

3. Event-Driven Hypergraph Memory with STDP-Inspired Updates

• Hypergraph structure for multi-node relationships
• STDP-inspired prototype updates for memory formation
• Converges to optimal anomaly prototypes
• Captures higher-order temporal dependencies

4. Spike Rate Contrast Pooling

• Pooling based on spiking irregularity
• Provably achieves anomaly selection bounds
• Differentiates normal vs anomalous patterns through firing statistics

5. Adaptive STDP Layers

• Captures causal temporal relationships
• Biologically plausible learning mechanism
• Stable convergence guarantees
• No backpropagation required for these layers

6. Multi-Scale Temporal Convolution with Multi-Factor Fusion

• Multi-scale temporal feature extraction
• Multi-factor anomaly score fusion
• Up to 5x variance reduction in scores
• Calibrated anomaly detection output

Theoretical Guarantees

Component	Guarantee
Spike Encoding	Information preservation with linear resolution scaling
LIFGAT	Universal approximation of continuous attention functions
Hypergraph Memory	Convergence to optimal prototypes
Contrast Pooling	Provable anomaly selection bounds
STDP Learning	Stable convergence
Multi-Factor Fusion	Up to 5x variance reduction

Architecture Overview

[Dynamic Graph Input] 
    ↓
[Temporal Spike Graph Encoding (Adaptive LIF)]
    ↓
[LIF-based Graph Attention + Lateral Inhibition]
    ↓
[Event-Driven Hypergraph Memory (STDP Updates)]
    ↓
[Spike Rate Contrast Pooling]
    ↓
[Adaptive STDP Layers]
    ↓
[Multi-Scale Temporal Convolution]
    ↓
[Multi-Factor Anomaly Fusion]
    ↓
[Anomaly Score Output]

Applications

• Cybersecurity: Network intrusion detection
• Industrial Monitoring: Equipment fault detection
• Social Networks: Bot/fake account detection
• Financial Networks: Fraud detection
• IoT Networks: Anomalous device behavior

Significance for NeuroAI

1. Unifies spiking computation, STDP learning, and graph anomaly detection
2. Provides theoretical guarantees for each component
3. Energy-efficient for continuous monitoring on neuromorphic hardware
4. Biologically plausible learning without backpropagation
5. Validated on 9 datasets (both dynamic and static graphs)

Implementation Guidance

When to Use:

• Real-time anomaly detection on streaming graph data
• Deployment on neuromorphic hardware (Loihi, TrueNorth)
• Energy-constrained edge computing scenarios
• Applications requiring temporal pattern detection

Key Components to Implement:

1. Spike Graph Encoder:

   • Convert node/edge features to spike timing patterns
   • Adaptive LIF threshold adjustment
   • Preserve structural and temporal information

2. LIFGAT Module:

   • LIF neuron-based attention computation
   • Lateral inhibition mechanism
   • Temporal spike pattern matching

3. STDP Memory Layer:

   • Hebbian-like weight updates
   • Prototype formation and refinement
   • Event-driven memory consolidation

4. Multi-Factor Fusion:

   • Combine multiple anomaly indicators
   • Variance reduction techniques
   • Score calibration

Limitations & Open Questions

• Scalability to very large graphs (millions of nodes)
• Real-world hardware deployment benchmarks
• Comparison with latest GNN-based anomaly detectors
• Handling of attributed vs. unattributed graphs

Related Skills

• neuromorphic-continual-nuclear-ics
• snn-learning-survey
• geometry-aware-spiking-gnn
• spiking-neural-network-analysis
• stdp-bernoulli-message-passing
• multi-plasticity-snn-training

Activation Keywords

• astpd-gad
• neuromorphic graph anomaly detection
• adaptive STDP
• spiking graph neural network
• LIF graph attention
• STDP anomaly detection
• energy-efficient anomaly detection
• temporal graph anomaly
• spike graph encoding
• neuromorphic cybersecurity

References

• arXiv: https://arxiv.org/abs/2605.13863
• PDF: https://arxiv.org/pdf/2605.13863.pdf