Related papers: An Energy-Based View of Graph Neural Networks

An Energy-Based View of Graph Neural Networks

URL: http://arxiv.org/abs/2104.13492v1
Date: Tue, 27 Apr 2021 21:54:30 GMT
Title: An Energy-Based View of Graph Neural Networks
Authors: John Y. Shin, Prathamesh Dharangutte
Abstract summary: Graph neural networks are a popular variant of neural networks that work with graph-structured data. We propose a novel method to ensure generation over features as well as the adjacency matrix. Our approach obtains comparable discriminative performance while improving robustness.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Graph neural networks are a popular variant of neural networks that work with graph-structured data. In this work, we consider combining graph neural networks with the energy-based view of Grathwohl et al. (2019) with the aim of obtaining a more robust classifier. We successfully implement this framework by proposing a novel method to ensure generation over features as well as the adjacency matrix and evaluate our method against the standard graph convolutional network (GCN) architecture (Kipf & Welling (2016)). Our approach obtains comparable discriminative performance while improving robustness, opening promising new directions for future research for energy-based graph neural networks.

Related papers

Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
arXiv Detail & Related papers (2024-03-18T18:01:01Z)
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
arXiv Detail & Related papers (2024-01-17T13:04:23Z)
Generative Graph Neural Networks for Link Prediction [13.643916060589463]
Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. This paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.
arXiv Detail & Related papers (2022-12-31T10:07:19Z)
Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
arXiv Detail & Related papers (2022-09-23T05:24:18Z)
Spiking Graph Convolutional Networks [19.36064180392385]
SpikingGCN is an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. We show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis.
arXiv Detail & Related papers (2022-05-05T16:44:36Z)
Capsule Graph Neural Networks with EM Routing [8.632437524560133]
This paper proposed novel Capsule Graph Neural Networks that use the EM routing mechanism (CapsGNNEM) to generate high-quality graph embeddings. Experimental results on a number of real-world graph datasets demonstrate that the proposed CapsGNNEM outperforms nine state-of-the-art models in graph classification tasks.
arXiv Detail & Related papers (2021-10-18T06:23:37Z)
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs [17.823543937167848]
mGCMN is a novel framework which utilizes node feature information and the higher order local structure of the graph. It will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment.
arXiv Detail & Related papers (2020-07-31T04:18:20Z)
Graph Structure of Neural Networks [104.33754950606298]
We show how the graph structure of neural networks affect their predictive performance. A "sweet spot" of relational graphs leads to neural networks with significantly improved predictive performance. Top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks.
arXiv Detail & Related papers (2020-07-13T17:59:31Z)
Binarized Graph Neural Network [65.20589262811677]
We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
arXiv Detail & Related papers (2020-04-19T09:43:14Z)
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)

This list is automatically generated from the titles and abstracts of the papers in this site.