On Node Features for Graph Neural Networks

Graph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning process. However, this prevents graph neural network from being applied into featureless graphs. In this paper, we first a...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Duong, Chi Thang, Thanh Dat Hoang, Ha The Hien Dang, Quoc Viet Hung Nguyen, Aberer, Karl
Format: Article
Language:English
Subjects:
Graph neural networks
Graph representations
Graphical representations
Graphs
Learning
Neural networks
Nodes
Online Access:Get full text
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Summary:Graph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning process. However, this prevents graph neural network from being applied into featureless graphs. In this paper, we first analyze the effects of node features on the performance of graph neural network. We show that GNNs work well if there is a strong correlation between node features and node labels. Based on these results, we propose new feature initialization methods that allows to apply graph neural network to non-attributed graphs. Our experimental results show that the artificial features are highly competitive with real features.
ISSN:2331-8422