An Introduction to Robust Graph Convolutional Networks
- URL: http://arxiv.org/abs/2103.14807v1
- Date: Sat, 27 Mar 2021 04:47:59 GMT
- Title: An Introduction to Robust Graph Convolutional Networks
- Authors: Mehrnaz Najafi and Philip S. Yu
- Abstract summary: We propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data.
By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly.
- Score: 71.68610791161355
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph convolutional neural networks (GCNs) generalize tradition convolutional
neural networks (CNNs) from low-dimensional regular graphs (e.g., image) to
high dimensional irregular graphs (e.g., text documents on word embeddings).
Due to inevitable faulty data collection instruments, deceptive data
manipulation, or other system errors, the data might be error-contaminated.
Even a small amount of error such as noise can compromise the ability of GCNs
and render them inadmissible to a large extent. The key challenge is how to
effectively and efficiently employ GCNs in the presence of erroneous data. In
this paper, we propose a novel Robust Graph Convolutional Neural Networks for
possible erroneous single-view or multi-view data where data may come from
multiple sources. By incorporating an extra layers via Autoencoders into
traditional graph convolutional networks, we characterize and handle typical
error models explicitly. Experimental results on various real-world datasets
demonstrate the superiority of the proposed model over the baseline methods and
its robustness against different types of error.
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