Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks

CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virtual Event Ireland October, 2020, pp. 1435-1444, 2020.

Cited by: 0|Bibtex|Views36|DOI:https://doi.org/10.1145/3340531.3411872
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Abstract:

Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkable success of GCNs, it is difficult to train GCNs with insufficient supervision. When labeled data are limited, the performance of GCNs becomes unsatisfying for...More

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