sg-jepa-spiking-graph-embedding

name	sg-jepa-spiking-graph-embedding
version	1.0.0
description	Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
trigger_words	["sg-jepa","spiking graph embedding","dynamic graph learning","spiking neural networks"]
arxiv_id	2607.18412

SG-JEPA: Scalable and Efficient Joint Spiking Embedding Predictive Architecture

Overview

SG-JEPA is a self-supervised framework for learning embeddings on large-scale dynamic graphs using spiking neural networks. It avoids complex machinery like negative sampling, graph augmentations, and edge-level reconstruction while achieving competitive performance.

Key Components

1. Temporal Context-Target Partitioning

• Partitions nodes into context and target sets along the temporal dimension
• Learns embeddings that are predictive of each other using spatial-temporal information
• Eliminates need for complex reconstruction objectives

2. Coarse-to-Fine Spike Count Embeddings

• Encodes sequential inputs into spike count embeddings at multiple granularities
• Enables adaptation to varying computational constraints of downstream tasks
• Leverages spiking neuron efficiency for scalability

Implementation Steps

1. Data Preprocessing: Prepare your dynamic graph data with temporal edges
2. Temporal Partitioning: Implement context-target node partitioning along time dimension
3. Spiking Encoder: Build a spiking neural network encoder that generates coarse-to-fine spike count embeddings
4. Predictive Training: Train the model to predict target embeddings from context embeddings
5. Downstream Integration: Use learned embeddings for node classification, link prediction, or other tasks

Benefits

• Scales to graphs with millions of edges (demonstrated on 13M edge graph)
• Superior training efficiency compared to prior self-supervised methods
• Better memory scalability due to simplified architecture
• Competitive or superior performance on node classification tasks
• Avoids complex components like negative sampling and graph augmentations

Use Cases

• Fraud detection in financial transaction graphs
• Recommender systems with evolving user-item interactions
• Social network analysis with temporal dynamics
• Any large-scale dynamic graph learning scenario

References

• Paper: Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
• Related work: JEPA, spiking neural networks, dynamic graph learning, self-supervised learning