Theoretically Expressive and Edge-aware Graph Learning

• URL: http://arxiv.org/abs/2001.09005v1
• Date: Fri, 24 Jan 2020 13:43:39 GMT
• Title: Theoretically Expressive and Edge-aware Graph Learning
• Authors: Federico Errica, Davide Bacciu, Alessio Micheli
• Abstract summary: We propose a new Graph Neural Network that combines recent advancements in the field. We prove that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network.
• Score: 24.954342094176013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged.

Related papers

• Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning [3.236774847052122]
  We study the expressive power of graph neural networks through the lens of graph partitioning. We introduce a novel GNN architecture, namely Graph Partitioning Neural Networks (GPNNs)
  arXiv  Detail & Related papers  (2023-12-14T06:08:35Z)
• Boosting Graph Structure Learning with Dummy Nodes [41.83708114701956]
  We extend graph kernels and graph neural networks with dummy nodes and conduct experiments on graph classification and subgraph isomorphism matching tasks. We prove that such a dummy node can help build an efficient monomorphic edge-to-vertex transform and an epimorphic inverse to recover the original graph back.
  arXiv  Detail & Related papers  (2022-06-17T05:44:24Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• GraphSVX: Shapley Value Explanations for Graph Neural Networks [81.83769974301995]
  Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data. In this paper, we propose a unified framework satisfied by most existing GNN explainers. We introduce GraphSVX, a post hoc local model-agnostic explanation method specifically designed for GNNs.
  arXiv  Detail & Related papers  (2021-04-18T10:40:37Z)
• Learning Graph Representations [0.0]
  Graph Neural Networks (GNNs) are efficient ways to get insight into large dynamic graph datasets. In this paper, we discuss the graph convolutional neural networks graph autoencoders and Social-temporal graph neural networks.
  arXiv  Detail & Related papers  (2021-02-03T12:07:55Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Natural Graph Networks [80.77570956520482]
  We show that the more general concept of naturality is sufficient for a graph network to be well-defined. We define global and local natural graph networks, the latter of which are as scalable as conventional message passing graph neural networks.
  arXiv  Detail & Related papers  (2020-07-16T14:19:06Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Geometrically Principled Connections in Graph Neural Networks [66.51286736506658]
  We argue geometry should remain the primary driving force behind innovation in the emerging field of geometric deep learning. We relate graph neural networks to widely successful computer graphics and data approximation models: radial basis functions (RBFs) We introduce affine skip connections, a novel building block formed by combining a fully connected layer with any graph convolution operator.
  arXiv  Detail & Related papers  (2020-04-06T13:25:46Z)
• Convolutional Kernel Networks for Graph-Structured Data [37.13712126432493]
  We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, by representing graphs as a sequence of kernel feature maps. Our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks.
  arXiv  Detail & Related papers  (2020-03-11T09:44:03Z)

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.