GPN: A Joint Structural Learning Framework for Graph Neural Networks

• URL: http://arxiv.org/abs/2205.05964v1
• Date: Thu, 12 May 2022 09:06:04 GMT
• Title: GPN: A Joint Structural Learning Framework for Graph Neural Networks
• Authors: Qianggang Ding, Deheng Ye, Tingyang Xu, Peilin Zhao
• Abstract summary: We propose a GNN-based joint learning framework that simultaneously learns the graph structure and the downstream task. Our method is the first GNN-based bilevel optimization framework for resolving this task.
• Score: 36.38529113603987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) have been applied into a variety of graph tasks. Most existing work of GNNs is based on the assumption that the given graph data is optimal, while it is inevitable that there exists missing or incomplete edges in the graph data for training, leading to degraded performance. In this paper, we propose Generative Predictive Network (GPN), a GNN-based joint learning framework that simultaneously learns the graph structure and the downstream task. Specifically, we develop a bilevel optimization framework for this joint learning task, in which the upper optimization (generator) and the lower optimization (predictor) are both instantiated with GNNs. To the best of our knowledge, our method is the first GNN-based bilevel optimization framework for resolving this task. Through extensive experiments, our method outperforms a wide range of baselines using benchmark datasets.

Related papers

• Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks [13.655670509818144]
  We propose a novel Graph structure Prompt Learning method (GPL) to enhance the training of Graph networks (GNNs) GPL employs task-independent graph structure losses to encourage GNNs to learn intrinsic graph characteristics while simultaneously solving downstream tasks. In experiments on eleven real-world datasets, after being trained by neural prediction, GNNs significantly outperform their original performance on node classification, graph classification, and edge tasks.
  arXiv  Detail & Related papers  (2024-07-16T03:59:18Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification [5.000404730573809]
  Graph Neural Networks (GNNs) have attracted increasing attention in recent years. Recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph. We propose a novel framework for semi-supervised classification.
  arXiv  Detail & Related papers  (2022-12-04T06:17:11Z)
• Self-Supervised Graph Structure Refinement for Graph Neural Networks [31.924317784535155]
  Graph structure learning (GSL) aims to learn the adjacency matrix for graph neural networks (GNNs) Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. We propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline.
  arXiv  Detail & Related papers  (2022-11-12T02:01:46Z)
• 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)
• Efficient and effective training of language and graph neural network models [36.00479096375565]
  We put forth an efficient and effective framework termed language model GNN (LM-GNN) to jointly train large-scale language models and graph neural networks. The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model. We evaluate the LM-GNN framework in different datasets performance and showcase the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2022-06-22T00:23:37Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• 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)
• Training Free Graph Neural Networks for Graph Matching [103.45755859119035]
  TFGM is a framework to boost the performance of Graph Neural Networks (GNNs) based graph matching without training. Applying TFGM on various GNNs shows promising improvements over baselines.
  arXiv  Detail & Related papers  (2022-01-14T09:04:46Z)
• Adaptive Kernel Graph Neural Network [21.863238974404474]
  Graph neural networks (GNNs) have demonstrated great success in representation learning for graph-structured data. In this paper, we propose a novel framework - i.e., namely Adaptive Kernel Graph Neural Network (AKGNN) AKGNN learns to adapt to the optimal graph kernel in a unified manner at the first attempt. Experiments are conducted on acknowledged benchmark datasets and promising results demonstrate the outstanding performance of our proposed AKGNN.
  arXiv  Detail & Related papers  (2021-12-08T20:23:58Z)
• Learning to Drop: Robust Graph Neural Network via Topological Denoising [50.81722989898142]
  We propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of Graph Neural Networks (GNNs) PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. We show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
  arXiv  Detail & Related papers  (2020-11-13T18:53:21Z)

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.