astdp-gad-neuromorphic-graph-anomaly

name

astdp-gad-neuromorphic-graph-anomaly

description

ASTDP-GAD: Neuromorphic Graph Anomaly Detection via Adaptive Spiking Temporal Dynamics Plasticity and Spiking Graph Neural Networks. Integrates STDP learning with spiking GNNs for energy-efficient anomaly detection in dynamic networks. Use when building neuromorphic anomaly detection, spiking graph neural networks, STDP-based learning on graphs, event-driven network monitoring, or energy-efficient graph ML. Trigger words: ASTDP-GAD, spiking graph neural network, neuromorphic anomaly detection, STDP graph learning, spiking graph attention, event-driven graph anomaly, adaptive STDP, LIF graph attention.

ASTDP-GAD: Neuromorphic Graph Anomaly Detection

Methodology from arXiv:2605.13863 (Fofanah et al., Apr 2026).

Core Idea

ASTDP-GAD integrates spiking graph neural networks with Spike-Timing-Dependent Plasticity (STDP) learning for energy-efficient neuromorphic anomaly detection in dynamic networks. Unifies spiking neural computation, STDP learning, and graph-based anomaly detection.

Key Innovations

1. Temporal Spike Graph Encoding with Adaptive LIF

• Encodes dynamic graph data into spike trains using adaptive Leaky Integrate-and-Fire dynamics
• Preserves input information with resolution scaling linearly in simulation steps and hidden dimension
• Captures temporal evolution of graph structures through spike timing patterns

2. LIF-Based Graph Attention (LIFGAT) with Lateral Inhibition

• Graph attention mechanism implemented with LIF neurons
• Approximates any continuous attention function (theoretical guarantee)
• Lateral inhibition enables competitive feature selection across neighbors
• Event-driven computation reduces energy compared to dense attention

3. Event-Driven Hypergraph Memory with STDP Prototype Updates

• Maintains hypergraph memory of normal patterns using STDP-inspired updates
• Converges to optimal prototypes (theoretical guarantee)
• Enables few-shot anomaly detection through prototype comparison

4. Spike Rate Contrast Pooling

• Pools information based on spiking irregularity metrics
• Achieves provable anomaly selection bounds
• Amplifies anomalous signals through contrast between normal and abnormal spike rates

5. Adaptive STDP Layers

• Captures causal temporal relationships in dynamic graphs
• Converges stably (theoretical guarantee)
• Adapts plasticity window based on temporal context

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

• Combines multiple temporal scales for robust detection
• Multi-factor anomaly fusion produces calibrated scores
• Up to 5× variance reduction in anomaly scores

Theoretical Guarantees

Component	Guarantee
Spike encoding	Information preservation, linear resolution scaling
LIFGAT	Universal approximation of continuous attention
Hypergraph memory	Convergence to optimal prototypes
Contrast pooling	Provable anomaly selection bounds
STDP learning	Stable convergence
Multi-factor fusion	Calibrated scores, up to 5× variance reduction

Implementation Guidance

When to Use

• Real-time anomaly detection in dynamic networks (cybersecurity, industrial monitoring)
• Energy-constrained graph ML for edge deployment
• Neuromorphic hardware deployment of graph algorithms
• Temporal graph anomaly detection with causal structure

Architecture Pipeline

1. Encode: Convert dynamic graph snapshots to spike trains via adaptive LIF
2. Attention: Apply LIFGAT with lateral inhibition for neighbor aggregation
3. Memory: Update hypergraph prototypes via STDP-inspired learning
4. Pool: Apply spike rate contrast pooling for anomaly amplification
5. STDP: Learn causal temporal relationships with adaptive plasticity
6. Fuse: Multi-scale temporal convolution with multi-factor fusion for final scores

Training Strategy

• Initialize with normal graph patterns to establish prototype memory
• Use STDP learning rule for unsupervised prototype adaptation
• Apply contrast pooling to separate anomalous from normal patterns
• Multi-factor fusion combines structural, temporal, and rate-based signals

Pitfalls

• STDP learning requires careful temporal window sizing; too narrow misses long-range dependencies, too broad loses temporal precision
• LIF parameters (leak rate, threshold) must be tuned per dataset; default values may not transfer
• Hypergraph memory capacity limits scalability; consider prototype pruning for large graphs
• Spike rate contrast pooling assumes sufficient spike count; very sparse graphs may need temporal window expansion

Related Skills

• spiking-neural-network-analysis
• snn-learning-survey
• stdp-spiking-transformer-attention
• geometry-aware-spiking-gnn