Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs

• URL: http://arxiv.org/abs/2607.18412v1
• Date: Mon, 20 Jul 2026 18:02:04 GMT
• Title: Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
• Abstract summary: We propose SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs.<n> SG-JEPA partitions nodes into context and target sets along the temporal dimension to learn embeddings that are predictive of each other.<n>Experiments demonstrate that SG-JEPA achieves competitive or even superior performance over discriminative baselines on node classification.
• Score: 43.39867023055097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems. Due to the scarcity of labeled data in real-world dynamic graphs, recent studies have introduced generative or contrastive paradigms (e.g., masked graph autoencoders or graph contrastive learning) to generate task-agnostic graph embeddings. However, these methods typically rely on complex edge-level reconstruction objectives and tailored graph augmentation strategies. This incurs substantial computational overhead when scaling to large-scale dynamic graphs. In this paper, we propose SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs. In contrast to existing self-supervised methods, SG-JEPA partitions nodes into context and target sets along the temporal dimension to learn embeddings that are predictive of each other via additional spatial-temporal information. Furthermore, through encoding sequential inputs into coarse-to-fine spike count embeddings, spiking neurons enable SG-JEPA to adapt to the varying computational constraints of downstream tasks. Extensive experiments demonstrate that SG-JEPA achieves competitive or even superior performance over discriminative baselines on node classification, while effectively scaling to the dynamic graph with 13 million edges. SG-JEPA avoids the complex machinery (negative sampling, graph augmentations, edge-level reconstruction, etc.), resulting in superior training efficiency and memory scalability compared with prior self-supervised dynamic graph baselines.

Related papers

• Dynamic Graph Condensation [40.099854631984556]
  temporal extension in dynamic graphs poses significant data efficiency challenges.<n>We propose DyGC, a framework that condenses the real dynamic graph into a compact version.<n>Our method retains up to 96.2% DGNN performance with only 0.5% of the original graph size, and achieves up to 1846 times training speedup.
  arXiv  Detail & Related papers  (2025-06-16T05:11:29Z)
• Scalable Graph Generative Modeling via Substructure Sequences [50.32639806800683]
  We introduce Generative Graph Pattern Machine (G$2$PM), a generative Transformer pre-training framework for graphs.<n>G$2$PM represents graph instances (nodes, edges, or entire graphs) as sequences of substructures.<n>It employs generative pre-training over the sequences to learn generalizable and transferable representations.
  arXiv  Detail & Related papers  (2025-05-22T02:16:34Z)
• RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs [58.10503898336799]
  We introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline.<n>RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components.<n>Our evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems.
  arXiv  Detail & Related papers  (2025-03-25T03:21:48Z)
• Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
  We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)<n>This framework provides a standardized setting to evaluate GNNs across diverse datasets.<n>We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models [16.435352947791923]
  We propose a novel Dynamic Graph structure learning framework with the Selective State Space Models (Mamba)<n>Our framework is superior to state-of-the-art baselines against adversarial attacks.
  arXiv  Detail & Related papers  (2024-12-11T07:32:38Z)
• DHIL-GT: Scalable Graph Transformer with Decoupled Hierarchy Labeling [10.556366638048384]
  Graph Transformer (GT) has recently emerged as a promising neural network architecture for learning graph-structured data.<n>We propose DHIL-GT, a scalable Graph Transformer that simplifies network learning by fully decoupling the graph computation to a separate stage in advance.<n> DHIL-GT is efficient in terms of computational boost and mini-batch capability over existing scalable Graph Transformer designs on large-scale benchmarks.
  arXiv  Detail & Related papers  (2024-12-06T02:59:01Z)
• LLM-Based Multi-Agent Systems are Scalable Graph Generative Models [73.28294528654885]
  GraphAgent-Generator (GAG) is a novel simulation-based framework for dynamic, text-attributed social graph generation.<n>GAG simulates the temporal node and edge generation processes for zero-shot social graph generation.<n>The resulting graphs exhibit adherence to seven key macroscopic network properties, achieving an 11% improvement in microscopic graph structure metrics.
  arXiv  Detail & Related papers  (2024-10-13T12:57:08Z)
• Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs [1.3571543090749625]
  We introduce a constrained probabilistic generative model to generate informative subgraphs that guide the evolution of dynamic graphs. The informative subgraph identified by DyGIS will serve as the input of dynamic graph masked autoencoder (DGMAE)
  arXiv  Detail & Related papers  (2024-09-14T02:16:00Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• Efficient Dynamic Graph Representation Learning at Scale [66.62859857734104]
  We propose Efficient Dynamic Graph lEarning (EDGE), which selectively expresses certain temporal dependency via training loss to improve the parallelism in computations. We show that EDGE can scale to dynamic graphs with millions of nodes and hundreds of millions of temporal events and achieve new state-of-the-art (SOTA) performance.
  arXiv  Detail & Related papers  (2021-12-14T22:24:53Z)
• Dynamic Graph Representation Learning via Graph Transformer Networks [41.570839291138114]
  We propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) DGT has spatial-temporal encoding to effectively learn graph topology and capture implicit links. We show that DGT presents superior performance compared with several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-11-19T21:44:23Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.