DeepAI AI Chat
Log In Sign Up

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

08/14/2019
by   Ke Sun, et al.
Peking University
0

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN (AdaBoosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors and integrate knowledge from different hops of neighbors into the network in an AdaBoost way. We also present the architectural difference between AdaGCN and existing graph convolutional methods to show the benefits of our proposal. Finally, extensive experiments demonstrate the state-of-the-art prediction performance and the computational advantage of our approach AdaGCN.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/09/2021

Non-Recursive Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are powerful models for node represe...
05/22/2022

Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling

Intracranial arteries are critical blood vessels that supply the brain w...
02/17/2023

Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks

Multiple recent studies show a paradox in graph convolutional networks (...
12/19/2019

Graph Convolutional Networks: analysis, improvements and results

In the current era of neural networks and big data, higher dimensional d...
01/11/2023

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

Graph convolutional networks (GCNs) have been attracting widespread atte...
06/04/2019

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

This paper introduces a new learning-based approach for approximately so...
06/11/2022

Parameter Convex Neural Networks

Deep learning utilizing deep neural networks (DNNs) has achieved a lot o...