Fake detection in imbalance dataset by Semi-supervised learning with GAN
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As social media grows faster, harassment becomes more prevalent which leads to considered fake detection a fascinating field among researchers. The graph nature of data with the large number of nodes caused different obstacles including a considerable amount of unrelated features in matrices as high dispersion and imbalance classes in the dataset. To deal with these issues Auto-encoders and a combination of semi-supervised learning and the GAN algorithm which is called SGAN were used. This paper is deploying a smaller number of labels and applying SGAN as a classifier. The result of this test showed that the accuracy had reached 91% in detecting fake accounts using only 100 labeled samples.
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