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arxiv: 2411.11259 · v3 · submitted 2024-11-18 · 💻 cs.LG

Graph Retention Networks for Dynamic Graphs

Qian Chang , Xia Li , Xiufeng Cheng , Runsong Jia , Jinqing Yang , Guoping Hu , Ciprian Doru Giurcaneanu This is my paper

Pith reviewed 2026-05-23 17:41 UTC · model grok-4.3

classification 💻 cs.LG
keywords dynamic graphsretention networksgraph neural networksparallel trainingefficient inferenceedge predictionnode classificationscalability
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The pith

Graph Retention Networks extend retention to dynamic graphs for parallel training and O(1) inference.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Graph Retention Networks (GRNs) to handle dynamic graphs by extending retention mechanisms. This equips models with parallelizable training, low-cost constant-time inference, and chunkwise long-term training. A reader would care if this delivers better efficiency without sacrificing performance on tasks like edge prediction and node classification. Experiments claim significant reductions in latency and memory with inference speedups up to 86.7 times over baselines.

Core claim

Graph Retention Networks (GRNs) are proposed as a unified architecture for deep learning on dynamic graphs. By extending retention into graph retention, the model gains three computational paradigms: parallelizable training, O(1) inference, and long-term chunkwise training. This achieves an optimal balance between efficiency, effectiveness, and scalability, with competitive performance on benchmark datasets for edge-level prediction and node-level classification tasks.

What carries the argument

The graph retention mechanism that adapts retention concepts to dynamic graph data to enable parallelizable training, O(1) inference, and chunkwise training.

Load-bearing premise

The retention mechanism extends to dynamic graph structures while preserving parallel training, O(1) inference, and chunkwise properties without extra overheads or loss of expressiveness.

What would settle it

Experiments on standard benchmarks where GRN does not show reduced training latency or memory use and fails to achieve the claimed inference speedups while matching performance.

Figures

Figures reproduced from arXiv: 2411.11259 by Ciprian Doru Giurcaneanu, Guoping Hu, Jinqing Yang, Qian Chang, Runsong Jia, Xia Li, Xiufeng Cheng.

Figure 1
Figure 1. Figure 1: Architecture of GRNs. retention paradigm for the 𝑚-th stage is formulated as follows: Q[𝑚] =X 𝑖 𝐵𝑚:𝐵(𝑚+1)W𝑞 + 1 ⊤ b𝑞, K[𝑚] =X 𝑗 𝐵𝑚:𝐵(𝑚+1)W𝑘 + 1 ⊤ b𝑘, V[𝑚] =X 𝑗 𝐵𝑚:𝐵(𝑚+1)W𝑣 + 1 ⊤ b𝑣 R[𝑚] =𝜆 𝐵R[𝑚−1] + K ⊺ [𝑚] V[𝑚] GraphRetention(x 𝑖 , X 𝑗 ) = (Q[𝑚]K ⊺ [𝑚] ⊙ 𝐷)V[𝑚] | {z } current chunk +𝜆 𝐵Q[𝑚]R[𝑚−1] | {z } past chunk (6) where 𝐵 is the chunk size and W𝑞, W𝑘, W𝑣 are identical to those defined in Equation (2).… view at source ↗
Figure 2
Figure 2. Figure 2: Training and inference efficiency comparison. SPE = Seconds Per Epoch; MB = MegaByte; SPS = Samples Per Second. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: 2D (Filled Contour) and 3D (Surface) loss landscape on Wikipedia dataset. We show the top-ranked performers here, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: T-SNE visualization of recurrent states with [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Results of ablation experiments on GRN compo [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: T-SNE visualization of re￾current states with 𝜆 = 1−2 −10. Each stage represents the same 5% inter￾val of the input data within a train￾ing epoch. 4 5 6 7 8 9 10 75 80 85 90 95 100 Wikipedia Reddit MOOC UN Vote Contact AP (%) [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
read the original abstract

In this paper, we propose Graph Retention Networks (GRNs) as a unified architecture for deep learning on dynamic graphs. The GRN extends the concept of retention into dynamic graph data as graph retention, equipping the model with three key computational paradigms: parallelizable training, low-cost $\mathcal{O}(1)$ inference, and long-term chunkwise training. This architecture achieves an optimal balance between efficiency, effectiveness, and scalability. Extensive experiments on benchmark datasets demonstrate its strong performance in both edge-level prediction and node-level classification tasks with significantly reduced training latency, lower GPU memory overhead, and improved inference throughput by up to 86.7x compared to SOTA baselines. The proposed GRN architecture achieves competitive performance across diverse dynamic graph benchmarks, demonstrating its adaptability to a wide range of tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper proposes Graph Retention Networks (GRNs) as a unified architecture for deep learning on dynamic graphs. It extends the retention mechanism to graph retention, claiming three computational paradigms: parallelizable training, low-cost O(1) inference, and long-term chunkwise training. The architecture is asserted to achieve an optimal balance of efficiency, effectiveness, and scalability, with experiments on benchmark datasets showing competitive performance on edge-level prediction and node-level classification tasks alongside reduced training latency, lower GPU memory, and inference throughput improvements up to 86.7x over SOTA baselines.

Significance. If the extension of retention to dynamic graphs preserves the stated asymptotic properties without hidden recomputation costs, the work could provide a scalable alternative to existing dynamic graph models, with potential impact on temporal network applications requiring efficient long-term modeling.

major comments (1)
  1. [Architecture description (following abstract claims on graph retention)] The central claim that graph retention inherits parallel training, O(1) inference, and chunkwise properties from the base retention mechanism when applied to evolving graphs requires an explicit derivation of the node/edge update rule (likely in the architecture section) showing that it incurs no extra asymptotic cost or auxiliary structures; absent this, the 86.7x inference claim and asserted optimal balance do not necessarily follow.
minor comments (1)
  1. [Abstract] The abstract states 'significantly reduced training latency' and 'improved inference throughput by up to 86.7x' without specifying the exact baselines or conditions under which the maximum speedup is achieved; this should be clarified with reference to specific tables or figures.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the single major comment below regarding the need for an explicit derivation of the graph retention update rules.

read point-by-point responses

  1. Referee: [Architecture description (following abstract claims on graph retention)] The central claim that graph retention inherits parallel training, O(1) inference, and chunkwise properties from the base retention mechanism when applied to evolving graphs requires an explicit derivation of the node/edge update rule (likely in the architecture section) showing that it incurs no extra asymptotic cost or auxiliary structures; absent this, the 86.7x inference claim and asserted optimal balance do not necessarily follow.

    Authors: We agree that an explicit derivation of the node/edge update rules is necessary to rigorously establish inheritance of the three computational paradigms without hidden costs. The current manuscript describes the extension at a high level but does not include the full step-by-step recurrence and chunkwise formulations for dynamic graphs. In the revised version we will add a dedicated subsection in the architecture section that derives the update rules from the base retention mechanism, proves the O(1) per-step inference cost, confirms parallel training complexity, and shows that no auxiliary structures or recomputation are introduced beyond standard graph operations. This addition will directly support the reported efficiency gains. revision: yes

Circularity Check

0 steps flagged

No circularity detected in GRN derivation chain

full rationale

The paper defines Graph Retention Networks by extending the retention mechanism to dynamic graphs via new architectural components for node/edge updates. The claimed properties (parallel training, O(1) inference, chunkwise training) are presented as following directly from these design choices and are validated through experiments on external benchmarks rather than by fitting parameters or self-referential definitions. No load-bearing self-citations, uniqueness theorems from prior author work, or reductions of predictions to inputs by construction appear in the abstract or described claims. The derivation remains self-contained with independent empirical content.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities; full text required for ledger construction.

pith-pipeline@v0.9.0 · 5675 in / 1038 out tokens · 26325 ms · 2026-05-23T17:41:54.673390+00:00 · methodology

discussion (0)

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