Exploiting Transductive Property of Graph Convolutional Neural Networks
with Less Labeling Effort
- URL: http://arxiv.org/abs/2105.13765v1
- Date: Sat, 1 May 2021 05:33:31 GMT
- Title: Exploiting Transductive Property of Graph Convolutional Neural Networks
with Less Labeling Effort
- Authors: Yasir Kilic
- Abstract summary: The developing GCN model has made significant experimental contributions with Convolution filters applied to graph data.
Due to its transductive property, all of the data samples, which is partially labeled, are given as input to the model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, machine learning approaches on Graph data have become very popular.
It was observed that significant results were obtained by including implicit or
explicit logical connections between data samples that make up the data to the
model. In this context, the developing GCN model has made significant
experimental contributions with Convolution filters applied to graph data. This
model follows Transductive and Semi-Supervised Learning approach. Due to its
transductive property, all of the data samples, which is partially labeled, are
given as input to the model. Labeling, which is a cost, is very important.
Within the scope of this study, the following research question is tried to be
answered: If at least how many samples are labeled, the optimum model success
is achieved? In addition, some experimental contributions have been made on the
accuracy of the model, whichever sampling approach is used with fixed labeling
effort. According to the experiments, the success of the model can be increased
by using the local centrality metric.
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