Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis

• URL: http://arxiv.org/abs/2410.08759v1
• Date: Fri, 11 Oct 2024 12:19:17 GMT
• Title: Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis
• Authors: Zhifei Li, Gerrit Großmann, Verena Wolf,
• Abstract summary: This study explores the impact of various graph transformations as pre-processing steps on the performance of common graph neural network (GNN) architectures across standard datasets. Our findings reveal that certain transformations, particularly those augmenting node features with centrality measures, consistently improve expressivity. However, these gains come with trade-offs, as methods like graph encoding, while enhancing expressivity, introduce numerical inaccuracies widely-used python packages.
• Score: 0.4069144210024563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, a wide variety of graph neural network (GNN) architectures have emerged, each with its own strengths, weaknesses, and complexities. Various techniques, including rewiring, lifting, and node annotation with centrality values, have been employed as pre-processing steps to enhance GNN performance. However, there are no universally accepted best practices, and the impact of architecture and pre-processing on performance often remains opaque. This study systematically explores the impact of various graph transformations as pre-processing steps on the performance of common GNN architectures across standard datasets. The models are evaluated based on their ability to distinguish non-isomorphic graphs, referred to as expressivity. Our findings reveal that certain transformations, particularly those augmenting node features with centrality measures, consistently improve expressivity. However, these gains come with trade-offs, as methods like graph encoding, while enhancing expressivity, introduce numerical inaccuracies widely-used python packages. Additionally, we observe that these pre-processing techniques are limited when addressing complex tasks involving 3-WL and 4-WL indistinguishable graphs.

Related papers

• 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) This framework provides a standardized setting to evaluate GNNs across diverse datasets. We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-01T08:48:53Z)
• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Scalable Expressiveness through Preprocessed Graph Perturbations [1.6574413179773764]
  Graph Neural Networks (GNNs) have emerged as the predominant method for analyzing graph-structured data. We introduce Scalable Expressiveness through Preprocessed Graph Perturbation (SE2P) SE2P offers a flexible balance between scalability and generalizability with four distinct configuration classes. Our results indicate that, depending on the chosen SE2P configuration, the model can enhance generalizability compared to benchmarks while achieving significant speed improvements of up to 8-fold.
  arXiv  Detail & Related papers  (2024-06-17T16:32:57Z)
• Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks [2.7523980737007414]
  We propose TADA, an efficient and effective front-mounted data augmentation framework for graph neural networks (GNNs) on high-degree graphs (HDGs) Under the hood, TADA includes two key modules: (i) feature expansion with structure embeddings, and (ii) topology- and attribute-aware graph sparsification. TADA considerably improves the predictive performance of mainstream GNN models on 8 real homophilic/heterophilic HDGs in terms of node classification.
  arXiv  Detail & Related papers  (2024-06-08T14:14:19Z)
• Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks [25.12261412297796]
  Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. We propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN)
  arXiv  Detail & Related papers  (2024-05-17T08:50:00Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass [10.839902229218577]
  Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. It is unknown how much the frameworks are 'eco-friendly' from a green computing perspective.
  arXiv  Detail & Related papers  (2022-11-06T04:22:19Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks [0.27195102129094995]
  We use a random graph generator to investigate how the graph size and structural properties affect the predictive performance of GNNs. We present specific evidence that the average node degree is a key feature in determining whether GNNs can generalize to unseen graphs. We propose a multi- module GNN framework that allows the network to adapt flexibly to new graphs by generalizing a single canonical nonlinear transformation over aggregated inputs.
  arXiv  Detail & Related papers  (2022-09-14T12:13:59Z)
• 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)
• Orthogonal Graph Neural Networks [53.466187667936026]
  Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations. stacking more convolutional layers significantly decreases the performance of GNNs. We propose a novel Ortho-GConv, which could generally augment the existing GNN backbones to stabilize the model training and improve the model's generalization performance.
  arXiv  Detail & Related papers  (2021-09-23T12:39:01Z)
• 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.