Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations

by   Dawei Leng, et al.

Graph neural networks are emerging as continuation of deep learning success w.r.t. graph data. Tens of different graph neural network variants have been proposed, most following a neighborhood aggregation scheme, where the node features are updated via aggregating features of its neighboring nodes from layer to layer. Though related research surges, the power of GNNs are still not on-par-with their counterpart CNNs in computer vision and RNNs in natural language processing. We rethink this problem from the perspective of information propagation, and propose to enhance information propagation among GNN layers by combining heterogeneous aggregations. We argue that as richer information are propagated from shallow to deep layers, the discriminative capability of features formulated by GNN can benefit from it. As our first attempt in this direction, a new generic GNN layer formulation and upon this a new GNN variant referred as HAG-Net is proposed. We empirically validate the effectiveness of HAG-Net on a number of graph classification benchmarks, and elaborate all the design options and criterions along with.



There are no comments yet.


page 1

page 2

page 3

page 4


Neighborhood Enlargement in Graph Neural Networks

Graph Neural Network (GNN) is an effective framework for representation ...

Graph neural networks for emulating crack coalescence and propagation in brittle materials

High-fidelity fracture mechanics simulations of multiple microcracks int...

Graph Neural Network (GNN) in Image and Video Understanding Using Deep Learning for Computer Vision Applications

Graph neural networks (GNNs) is an information - processing system that ...

NDGGNET-A Node Independent Gate based Graph Neural Networks

Graph Neural Networks (GNNs) is an architecture for structural data, and...

Pyramidal Reservoir Graph Neural Network

We propose a deep Graph Neural Network (GNN) model that alternates two t...

A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs

We present an end-to-end deep learning segmentation method by combining ...

Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks

In this paper, we study a novel meta aggregation scheme towards binarizi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.