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 inarXiv.org
Main Authors Duong, Chi Thang, Thanh Dat Hoang, Ha The Hien Dang, Quoc Viet Hung Nguyen, Aberer, Karl
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.11.2019
Subjects
Graph neural networks
Graph representations
Graphical representations
Graphs
Learning
Neural networks
Nodes
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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