Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation
- URL: http://arxiv.org/abs/2510.10105v1
- Date: Sat, 11 Oct 2025 08:33:08 GMT
- Title: Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation
- Authors: Yanping Zheng, Zhewei Wei, Frank de Hoog, Xu Chen, Hongteng Xu, Yuhang Ye, Jiadeng Huang,
- Abstract summary: We propose textbfLighter-X, an efficient and modular framework that can be seamlessly integrated with existing GNN-based recommender architectures.<n>Our approach substantially reduces both parameter size and computational complexity while preserving the theoretical guarantees and empirical performance of the base models.<n>Experiments demonstrate that Lighter-X achieves comparable performance to baseline models with significantly fewer parameters.
- Score: 49.865020394064096
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in recommendation systems. However, conventional graph-based recommenders, such as LightGCN, require maintaining embeddings of size $d$ for each node, resulting in a parameter complexity of $\mathcal{O}(n \times d)$, where $n$ represents the total number of users and items. This scaling pattern poses significant challenges for deployment on large-scale graphs encountered in real-world applications. To address this scalability limitation, we propose \textbf{Lighter-X}, an efficient and modular framework that can be seamlessly integrated with existing GNN-based recommender architectures. Our approach substantially reduces both parameter size and computational complexity while preserving the theoretical guarantees and empirical performance of the base models, thereby enabling practical deployment at scale. Specifically, we analyze the original structure and inherent redundancy in their parameters, identifying opportunities for optimization. Based on this insight, we propose an efficient compression scheme for the sparse adjacency structure and high-dimensional embedding matrices, achieving a parameter complexity of $\mathcal{O}(h \times d)$, where $h \ll n$. Furthermore, the model is optimized through a decoupled framework, reducing computational complexity during the training process and enhancing scalability. Extensive experiments demonstrate that Lighter-X achieves comparable performance to baseline models with significantly fewer parameters. In particular, on large-scale interaction graphs with millions of edges, we are able to attain even better results with only 1\% of the parameter over LightGCN.
Related papers
- Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings [73.44599934855067]
LookaHES is a nonmyopic BO framework designed for dynamic, history-dependent cost environments.<n>LookaHES combines a multi-step variant of $H$-Entropy Search with pathwise sampling and neural policy optimization.<n>Our innovation is the integration of neural policies, including large language models, to effectively navigate structured, domain-specific action spaces.
arXiv Detail & Related papers (2026-01-10T09:49:45Z) - A Distributed Training Architecture For Combinatorial Optimization [0.0]
We propose a distributed graph neural network (GNN)-based training framework for optimization.<n>Experiments are conducted on both real large-scale social network datasets and synthetically generated high-complexity graphs.<n>Our framework outperforms state-of-the-art approaches in both solution quality and computational efficiency.
arXiv Detail & Related papers (2025-11-12T12:22:10Z) - ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion [73.85920403511706]
We propose ScaleGNN, a novel framework that adaptively fuses multi-hop node features for scalable and effective graph learning.<n>We show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency.
arXiv Detail & Related papers (2025-04-22T14:05:11Z) - Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning? [45.58422897857411]
This work explores the use of large language models (LLMs) for hyperparameter optimization by fine-tuning a parameter-efficient version of Code Llama using LoRA.<n>Our approach achieves competitive or superior Root Mean Square Error (RMSE) while substantially reducing computational overhead.<n>Results demonstrate that LLM-based optimization not only rivals established Bayesian methods like Tree-structured Parzen Estimators (TPE) but also accelerates tuning for real-world applications requiring perceptual quality and low-latency processing.
arXiv Detail & Related papers (2025-04-08T13:15:47Z) - 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) - ZeroLM: Data-Free Transformer Architecture Search for Language Models [54.83882149157548]
Current automated proxy discovery approaches suffer from extended search times, susceptibility to data overfitting, and structural complexity.<n>This paper introduces a novel zero-cost proxy methodology that quantifies model capacity through efficient weight statistics.<n>Our evaluation demonstrates the superiority of this approach, achieving a Spearman's rho of 0.76 and Kendall's tau of 0.53 on the FlexiBERT benchmark.
arXiv Detail & Related papers (2025-03-24T13:11:22Z) - LightGNN: Simple Graph Neural Network for Recommendation [14.514770044236375]
Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation.<n>Existing GNN paradigms face challenges in scalability and robustness when handling large-scale, noisy, and real-world datasets.<n>We present LightGNN, a lightweight and distillation-based GNN pruning framework.
arXiv Detail & Related papers (2025-01-06T18:59:55Z) - DepGraph: Towards Any Structural Pruning [68.40343338847664]
We study general structural pruning of arbitrary architecture like CNNs, RNNs, GNNs and Transformers.
We propose a general and fully automatic method, emphDependency Graph (DepGraph), to explicitly model the dependency between layers and comprehensively group parameters for pruning.
In this work, we extensively evaluate our method on several architectures and tasks, including ResNe(X)t, DenseNet, MobileNet and Vision transformer for images, GAT for graph, DGCNN for 3D point cloud, alongside LSTM for language, and demonstrate that, even with a
arXiv Detail & Related papers (2023-01-30T14:02:33Z) - GNN at the Edge: Cost-Efficient Graph Neural Network Processing over
Distributed Edge Servers [24.109721494781592]
Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions.
This paper studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network.
We show that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed.
arXiv Detail & Related papers (2022-10-31T13:03:16Z) - Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural
Networks [52.566735716983956]
We propose a graph gradual pruning framework termed CGP to dynamically prune GNNs.
Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs.
Our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.
arXiv Detail & Related papers (2022-07-18T14:23:31Z)
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.