Learning to Control the Smoothness of Graph Convolutional Network Features

• URL: http://arxiv.org/abs/2410.14604v1
• Date: Fri, 18 Oct 2024 16:57:27 GMT
• Title: Learning to Control the Smoothness of Graph Convolutional Network Features
• Authors: Shih-Hsin Wang, Justin Baker, Cory Hauck, Bao Wang,
• Abstract summary: We propose a new strategy to let graph convolutional network (GCN) learn node features with a desired smoothness. Our approach has three key steps: We establish a geometric relationship between the input and output of ReLU or leaky ReLU. Building on our geometric insights, we augment the message-passing process of graph convolutional layers with a learnable term to modulate the smoothness of node features.
• Score: 9.949988676706418
• License:
• Abstract: The pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Their results reveal an intricate empirical correlation between node classification accuracy and the ratio of smooth to non-smooth feature components. However, the optimal ratio that favors node classification is unknown, and the non-smooth features of deep GCN with ReLU or leaky ReLU activation function diminish. In this paper, we propose a new strategy to let GCN learn node features with a desired smoothness -- adapting to data and tasks -- to enhance node classification. Our approach has three key steps: (1) We establish a geometric relationship between the input and output of ReLU or leaky ReLU. (2) Building on our geometric insights, we augment the message-passing process of graph convolutional layers (GCLs) with a learnable term to modulate the smoothness of node features with computational efficiency. (3) We investigate the achievable ratio between smooth and non-smooth feature components for GCNs with the augmented message-passing scheme. Our extensive numerical results show that the augmented message-passing schemes significantly improve node classification for GCN and some related models.

Related papers

• Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks [58.050130177241186]
  Noise perturbations often corrupt 3-D point clouds, hindering downstream tasks such as surface reconstruction, rendering, and further processing. This paper introduces finegranularity dynamic graph convolutional networks called GDGCN, a novel approach to denoising in 3-D point clouds.
  arXiv  Detail & Related papers  (2024-11-21T14:19:32Z)
• Scalable Graph Compressed Convolutions [68.85227170390864]
  We propose a differentiable method that applies permutations to calibrate input graphs for Euclidean convolution. Based on the graph calibration, we propose the Compressed Convolution Network (CoCN) for hierarchical graph representation learning.
  arXiv  Detail & Related papers  (2024-07-26T03:14:13Z)
• LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference [19.5669231249754]
  We present LinGCN, a framework designed to reduce multiplication depth and optimize the performance of HE based GCN inference. Remarkably, LinGCN achieves a 14.2x latency speedup relative to CryptoGCN, while preserving an inference accuracy of 75% and notably reducing multiplication depth.
  arXiv  Detail & Related papers  (2023-09-25T17:56:54Z)
• 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)
• Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification [6.619370466850894]
  We study the problem of graph data augmentation for Graph Convolutional Network (GCN) Specifically, we conduct cosine similarity based cross operation on the original features to create new graph features, including new node attributes. We also propose an attentional integrating model to weighted sum the hidden node embeddings encoded by these GCNs into the final node embeddings.
  arXiv  Detail & Related papers  (2021-06-16T15:13:51Z)
• Towards Efficient Graph Convolutional Networks for Point Cloud Handling [181.59146413326056]
  We aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. A series of experiments show that optimized networks have reduced computational complexity, decreased memory consumption, and accelerated inference speed.
  arXiv  Detail & Related papers  (2021-04-12T17:59:16Z)
• Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering [10.905770964670191]
  We propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions. As an alternative to graph-level smoothness, a novel fine-gained node-wise level assessment of smoothness is proposed. Experiments show that the proposed methods significantly outperform 12 other state-of-the-art baselines in terms of three different metrics.
  arXiv  Detail & Related papers  (2020-09-12T08:12:27Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [85.0332394224503]
  We study whether Graph Convolutional Networks (GCNs) can optimally integrate node features and topological structures in a complex graph with rich information. We propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN) Our experiments show that AM-GCN extracts the most correlated information from both node features and topological structures substantially.
  arXiv  Detail & Related papers  (2020-07-05T08:16:03Z)

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.