Clenshaw Graph Neural Networks

• URL: http://arxiv.org/abs/2210.16508v1
• Date: Sat, 29 Oct 2022 06:32:39 GMT
• Title: Clenshaw Graph Neural Networks
• Authors: Yuhe Guo and Zhewei Wei
• Abstract summary: Graph Convolutional Networks (GCNs) are foundational methods for learning graph representations. Existing residual connection techniques fail to make extensive use of underlying graph structure. We introduce ClenshawGCN, a GNN model that employs the Clenshaw Summation algorithm to enhance the expressiveness of the GCN model.
• Score: 14.8308791628821
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. Existing residual connection techniques, however, fail to make extensive use of underlying graph structure as in the graph spectral domain, which is critical for obtaining satisfactory results on heterophilic graphs. In this paper, we introduce ClenshawGCN, a GNN model that employs the Clenshaw Summation Algorithm to enhance the expressiveness of the GCN model. ClenshawGCN equips the standard GCN model with two straightforward residual modules: the adaptive initial residual connection and the negative second-order residual connection. We show that by adding these two residual modules, ClenshawGCN implicitly simulates a polynomial filter under the Chebyshev basis, giving it at least as much expressive power as polynomial spectral GNNs. In addition, we conduct comprehensive experiments to demonstrate the superiority of our model over spatial and spectral GNN models.

Related papers

• Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification [12.062421384484812]
  Graph Convolutional Network (GCN) decouples neighborhood aggregation and feature transformation in each convolutional layer. In this paper, we propose a new paradigm of GCN, termed Neighborhood Convolutional Network (NCN) In this way, the model could inherit the merit of decoupled GCN for aggregating neighborhood information, at the same time, develop much more powerful feature learning modules.
  arXiv  Detail & Related papers  (2022-11-15T02:02:51Z)
• Learnable Filters for Geometric Scattering Modules [64.03877398967282]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2022-08-15T22:30:07Z)
• Deep Manifold Learning with Graph Mining [80.84145791017968]
  We propose a novel graph deep model with a non-gradient decision layer for graph mining. The proposed model has achieved state-of-the-art performance compared to the current models.
  arXiv  Detail & Related papers  (2022-07-18T04:34:08Z)
• EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks [51.42338058718487]
  Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs. We propose EvenNet, a spectral GNN corresponding to an even-polynomial graph filter.
  arXiv  Detail & Related papers  (2022-05-27T10:48:14Z)
• Image-Like Graph Representations for Improved Molecular Property Prediction [7.119677737397071]
  We propose a new intrinsic molecular representation that bypasses the need for GNNs entirely, dubbed CubeMol. Our fixed-dimensional representation, when paired with a transformer model, exceeds the performance of state-of-the-art GNN models and provides a path for scalability.
  arXiv  Detail & Related papers  (2021-11-20T22:39:11Z)
• SStaGCN: Simplified stacking based graph convolutional networks [6.742080381542375]
  Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks. We propose a novel GCN called SStaGCN (Simplified stacking based GCN) by utilizing the ideas of stacking and aggregation. We show that SStaGCN can efficiently mitigate the over-smoothing problem of GCN.
  arXiv  Detail & Related papers  (2021-11-16T05:00:08Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)
• Simple and Deep Graph Convolutional Networks [63.76221532439285]
  Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Despite their success, most of the current GCN models are shallow, due to the em over-smoothing problem. We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques.
  arXiv  Detail & Related papers  (2020-07-04T16:18:06Z)
• Directed Graph Convolutional Network [15.879411956536885]
  We extend spectral-based graph convolution to directed graphs by using first- and second-order proximity. A new GCN model, called DGCN, is then designed to learn representations on the directed graph.
  arXiv  Detail & Related papers  (2020-04-29T06:19:10Z)
• Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks [0.0]
  Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features. Here, we propose to augment conventional GCNs with geometric scattering transforms and residual convolutions. The former enables band-pass filtering of graph signals, thus alleviating the so-called oversmoothing often encountered in GCNs.
  arXiv  Detail & Related papers  (2020-03-18T18:03:08Z)
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [55.44107800525776]
  Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models. In this paper, we revisit GCN based Collaborative Filtering (CF) based Recommender Systems (RS) We show that removing non-linearities would enhance recommendation performance, consistent with the theories in simple graph convolutional networks. We propose a residual network structure that is specifically designed for CF with user-item interaction modeling.
  arXiv  Detail & Related papers  (2020-01-28T04:41:25Z)

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.