AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning

• URL: http://arxiv.org/abs/2512.18295v1
• Date: Sat, 20 Dec 2025 09:55:36 GMT
• Title: AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
• Authors: Xuling Zhang, Jindong Li, Yifei Zhang, Menglin Yang,
• Abstract summary: Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge.<n>We propose AL GNN, a novel framework for continual graph learning that eliminates the need for backpropagation and replay buffers.
• Score: 8.911446190681882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge. Existing methods particularly those based on experience replay typically store and revisit past graph data to mitigate catastrophic forgetting. However, these approaches pose significant limitations, including privacy concerns, inefficiency. In this work, we propose AL GNN, a novel framework for continual graph learning that eliminates the need for backpropagation and replay buffers. Instead, AL GNN leverages principles from analytic learning theory to formulate learning as a recursive least squares optimization process. It maintains and updates model knowledge analytically through closed form classifier updates and a regularized feature autocorrelation matrix. This design enables efficient one pass training for each task, and inherently preserves data privacy by avoiding historical sample storage. Extensive experiments on multiple dynamic graph classification benchmarks demonstrate that AL GNN achieves competitive or superior performance compared to existing methods. For instance, it improves average performance by 10% on CoraFull and reduces forgetting by over 30% on Reddit, while also reducing training time by nearly 50% due to its backpropagation free design.

Related papers

• Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs [62.544129365882014]
  We propose a novel Semi-supervised Instruction Tuning pipeline for Graph Learning, named SIT-Graph.<n> SIT-Graph is model-agnostic and can be seamlessly integrated into any graph instruction tuning method that utilizes LLMs as the predictor.<n>Extensive experiments demonstrate that when incorporated into state-of-the-art graph instruction tuning methods, SIT-Graph significantly enhances their performance on text-attributed graph benchmarks.
  arXiv  Detail & Related papers  (2026-01-19T08:10:53Z)
• Prompt-Driven Continual Graph Learning [35.58675758528851]
  Continual Graph Learning (CGL) aims to accommodate new tasks over evolving graph data without forgetting prior knowledge.<n>This paper introduces a novel prompt-driven continual graph learning framework, which learns a separate prompt for each incoming task and maintains the underlying graph neural network model fixed.
  arXiv  Detail & Related papers  (2025-02-10T10:28:11Z)
• FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening [1.1345413192078595]
  This paper presents a novel approach to improve the scalability of Graph Neural Networks (GNNs) by reducing computational burden during the inference phase using graph coarsening.<n>Our study extends the application of graph coarsening for graph-level tasks, including graph classification and graph regression.<n>Results show that the proposed method achieves orders of magnitude improvements in single-node inference time compared to traditional approaches.
  arXiv  Detail & Related papers  (2024-10-19T06:27:24Z)
• Graph Continual Learning with Debiased Lossless Memory Replay [24.67200419367994]
  Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this problem by continually adapting GNNs to the expanded graph of the current task while maintaining the performance over the graph of previous tasks. Memory replay-based methods, which aim to replay data of previous tasks when learning new tasks, have been explored as one principled approach to mitigate the forgetting of the knowledge learned from the previous tasks. In this paper we extend this methodology with a novel framework, called Debiased Lossless Memory replay (DeLo
  arXiv  Detail & Related papers  (2024-04-17T01:31:00Z)
• Loss-aware Curriculum Learning for Heterogeneous Graph Neural Networks [30.333265803394998]
  This paper investigates the application of curriculum learning techniques to improve the performance of Heterogeneous Graph Neural Networks (GNNs) To better classify the quality of the data, we design a loss-aware training schedule, named LTS, that measures the quality of every nodes of the data. Our findings demonstrate the efficacy of curriculum learning in enhancing HGNNs capabilities for analyzing complex graph-structured data.
  arXiv  Detail & Related papers  (2024-02-29T05:44:41Z)
• A Topology-aware Graph Coarsening Framework for Continual Graph Learning [8.136809136959302]
  Continual learning on graphs tackles the problem of training a graph neural network (GNN) where graph data arrive in a streaming fashion. Traditional continual learning strategies such as Experience Replay can be adapted to streaming graphs. We propose TA$mathbbCO$, a (t)opology-(a)ware graph (co)arsening and (co)ntinual learning framework.
  arXiv  Detail & Related papers  (2024-01-05T22:22:13Z)
• Efficient Heterogeneous Graph Learning via Random Projection [58.4138636866903]
  Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN)
  arXiv  Detail & Related papers  (2023-10-23T01:25:44Z)
• How Graph Neural Networks Learn: Lessons from Training Dynamics [80.41778059014393]
  We study the training dynamics in function space of graph neural networks (GNNs) We find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function. This finding offers new interpretable insights into when and why the learned GNN functions generalize.
  arXiv  Detail & Related papers  (2023-10-08T10:19:56Z)
• Unlearning Graph Classifiers with Limited Data Resources [39.29148804411811]
  Controlled data removal is becoming an important feature of machine learning models for data-sensitive Web applications. It is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs) Our main contribution is the first known nonlinear approximate graph unlearning method based on GSTs. Our second contribution is a theoretical analysis of the computational complexity of the proposed unlearning mechanism. Our third contribution are extensive simulation results which show that, compared to complete retraining of GNNs after each removal request, the new GST-based approach offers, on average, a 10.38x speed-up
  arXiv  Detail & Related papers  (2022-11-06T20:46:50Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks [52.121819834353865]
  We show that for many standard transductive node classification benchmarks, we can exceed or match the performance of state-of-the-art GNNs. We call this overall procedure Correct and Smooth (C&S) Our approach exceeds or nearly matches the performance of state-of-the-art GNNs on a wide variety of benchmarks.
  arXiv  Detail & Related papers  (2020-10-27T02:10:52Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)

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.