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RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks

arXiv 2108.07481 ↗First posted 17 Aug 2021cs.LGcited by 0

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Abstract (arXiv)

Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training. Besides, the adjacency matrix of GCNs is stable, which makes the data processing strategy cannot efficiently adjust the quantity of training data from the built graph structures.To further improve the performance and the self-learning ability of GCNs,in this paper, we propose an efficient self-supervised learning strategy of GCNs,named randomly removed links with a fixed step at one region (RRLFSOR).RRLFSOR can be regarded as a new data augmenter to improve over-smoothing.RRLFSOR is examined on two efficient and representative GCN models with three public citation network datasets-Cora,PubMed,and Citeseer.Experiments on transductive link prediction tasks show that our strategy outperforms the baseline models consistently by up to 21.34% in terms of accuracy on three benchmark datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 64 references that are in the map. First posted on arXiv 17 Aug 2021.

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References · 40

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28 more
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