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VISAGNN: Versatile Staleness-Aware Efficient Training on Large-Scale Graphs

Rui Xue

TL;DR

This work tackles the staleness bottleneck in training large-scale GNNs with historical embeddings, which arise when out-of-batch neighbor information is approximated by stale embeddings. VISAGNN introduces three complementary components—Dynamic Staleness Attention, Staleness-aware Loss, and Staleness-Augmented Embeddings—to adaptively mitigate the negative impact of staleness during forward and backward passes, using a staleness criterion $s_j$ and a time-varying factor $\gamma(t) = \beta / t$. The authors provide theoretical analysis linking per-layer staleness to final embedding error, and prove convergence under the proposed mechanism; they also demonstrate strong empirical gains on large-scale datasets (e.g., ogbn-arxiv, ogbn-products, ogbn-papers100M, MAG240M) with favorable memory and convergence characteristics. Overall, VISAGNN offers a versatile, efficient, and principled framework that can enhance existing historical-embedding approaches and sampling strategies for scalable GNN training.

Abstract

Graph Neural Networks (GNNs) have shown exceptional success in graph representation learning and a wide range of real-world applications. However, scaling deeper GNNs poses challenges due to the neighbor explosion problem when training on large-scale graphs. To mitigate this, a promising class of GNN training algorithms utilizes historical embeddings to reduce computation and memory costs while preserving the expressiveness of the model. These methods leverage historical embeddings for out-of-batch nodes, effectively approximating full-batch training without losing any neighbor information-a limitation found in traditional sampling methods. However, the staleness of these historical embeddings often introduces significant bias, acting as a bottleneck that can adversely affect model performance. In this paper, we propose a novel VersatIle Staleness-Aware GNN, named VISAGNN, which dynamically and adaptively incorporates staleness criteria into the large-scale GNN training process. By embedding staleness into the message passing mechanism, loss function, and historical embeddings during training, our approach enables the model to adaptively mitigate the negative effects of stale embeddings, thereby reducing estimation errors and enhancing downstream accuracy. Comprehensive experiments demonstrate the effectiveness of our method in overcoming the staleness issue of existing historical embedding techniques, showcasing its superior performance and efficiency on large-scale benchmarks, along with significantly faster convergence.

VISAGNN: Versatile Staleness-Aware Efficient Training on Large-Scale Graphs

TL;DR

This work tackles the staleness bottleneck in training large-scale GNNs with historical embeddings, which arise when out-of-batch neighbor information is approximated by stale embeddings. VISAGNN introduces three complementary components—Dynamic Staleness Attention, Staleness-aware Loss, and Staleness-Augmented Embeddings—to adaptively mitigate the negative impact of staleness during forward and backward passes, using a staleness criterion and a time-varying factor . The authors provide theoretical analysis linking per-layer staleness to final embedding error, and prove convergence under the proposed mechanism; they also demonstrate strong empirical gains on large-scale datasets (e.g., ogbn-arxiv, ogbn-products, ogbn-papers100M, MAG240M) with favorable memory and convergence characteristics. Overall, VISAGNN offers a versatile, efficient, and principled framework that can enhance existing historical-embedding approaches and sampling strategies for scalable GNN training.

Abstract

Graph Neural Networks (GNNs) have shown exceptional success in graph representation learning and a wide range of real-world applications. However, scaling deeper GNNs poses challenges due to the neighbor explosion problem when training on large-scale graphs. To mitigate this, a promising class of GNN training algorithms utilizes historical embeddings to reduce computation and memory costs while preserving the expressiveness of the model. These methods leverage historical embeddings for out-of-batch nodes, effectively approximating full-batch training without losing any neighbor information-a limitation found in traditional sampling methods. However, the staleness of these historical embeddings often introduces significant bias, acting as a bottleneck that can adversely affect model performance. In this paper, we propose a novel VersatIle Staleness-Aware GNN, named VISAGNN, which dynamically and adaptively incorporates staleness criteria into the large-scale GNN training process. By embedding staleness into the message passing mechanism, loss function, and historical embeddings during training, our approach enables the model to adaptively mitigate the negative effects of stale embeddings, thereby reducing estimation errors and enhancing downstream accuracy. Comprehensive experiments demonstrate the effectiveness of our method in overcoming the staleness issue of existing historical embedding techniques, showcasing its superior performance and efficiency on large-scale benchmarks, along with significantly faster convergence.

Paper Structure

This paper contains 20 sections, 2 theorems, 11 equations, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Staleness of historical embedding methods
  5. Dynamic Staleness Attention
  6. Staleness-aware Loss
  7. Staleness-Augmented Embeddings
  8. Complexity Analysis
  9. Experiments
  10. Performance
  11. Efficiency Analysis
  12. Convergence Analysis
  13. Ablation Study
  14. Components
  15. Staleness resistance
  16. ...and 5 more sections

Key Result

Theorem 1

Assuming a L-layers GNN $g_\theta^{(l)}(h)$ with a Lipschitz constant $\beta^{(l)}$ for each layer $l = 1, \dots, L$, and $\mathcal{N}(i)$ is the set of neighbor nodes of $i$, $\forall i \in V$. $\|\bar{h}^{(l)} - h^{(l)}\|$ represents the distance between the historical embeddings and the true embe

Figures (3)

  • Figure 1: Three key designs in VISAGNN. (1) Augmented Embeddings: VISAGNN offers two ways to integrate staleness criterion into historical embeddings. (2) Dynamic Staleness Attention: VISAGNN performs weighted message passing based on both feature embeddings and staleness criterion. (3) Staleness-aware loss: A regularization term based on staleness is incorporated into the loss function in VISAGNN.
  • Figure 2: ogbn-arxiv
  • Figure 3: ogbn-products

Theorems & Definitions (2)

  • Theorem 1: Embeddings Approximation Error
  • Theorem 2