Graph Convolutional Networks for Graphs Containing Missing Features
- URL: http://arxiv.org/abs/2007.04583v2
- Date: Sun, 6 Dec 2020 13:07:35 GMT
- Title: Graph Convolutional Networks for Graphs Containing Missing Features
- Authors: Hibiki Taguchi, Xin Liu, Tsuyoshi Murata
- Abstract summary: We propose an approach that adapts Graph Convolutional Network (GCN) to graphs containing missing features.
In contrast to traditional strategy, our approach integrates the processing of missing features and graph learning within the same neural network architecture.
We demonstrate through extensive experiments that our approach significantly outperforms the imputation-based methods in node classification and link prediction tasks.
- Score: 5.426650977249329
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Convolutional Network (GCN) has experienced great success in graph
analysis tasks. It works by smoothing the node features across the graph. The
current GCN models overwhelmingly assume that the node feature information is
complete. However, real-world graph data are often incomplete and containing
missing features. Traditionally, people have to estimate and fill in the
unknown features based on imputation techniques and then apply GCN. However,
the process of feature filling and graph learning are separated, resulting in
degraded and unstable performance. This problem becomes more serious when a
large number of features are missing. We propose an approach that adapts GCN to
graphs containing missing features. In contrast to traditional strategy, our
approach integrates the processing of missing features and graph learning
within the same neural network architecture. Our idea is to represent the
missing data by Gaussian Mixture Model (GMM) and calculate the expected
activation of neurons in the first hidden layer of GCN, while keeping the other
layers of the network unchanged. This enables us to learn the GMM parameters
and network weight parameters in an end-to-end manner. Notably, our approach
does not increase the computational complexity of GCN and it is consistent with
GCN when the features are complete. We demonstrate through extensive
experiments that our approach significantly outperforms the imputation-based
methods in node classification and link prediction tasks. We show that the
performance of our approach for the case with a low level of missing features
is even superior to GCN for the case with complete features.
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