Abstract
Graphs are practiced widely in various real-world applications because of their structure modelling capability. Currently, deep neural networks have been employed to produce excellent outcomes in tasks such as classification. A graph convolutional network (GCN) is a deep learning model that operates on the graphs. We offer the general architecture of a GCN and its utilization in semi-supervised learning. We also investigate the application and performance of GCN in the classification of fake news. Finally, we present different application areas of GCN and open challenges for future research.
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Singh, R., Bathla, S., Meel, P. (2021). State-of-the-Art Applications of Graph Convolutional Neural Networks. In: Mahapatra, R.P., Panigrahi, B.K., Kaushik, B.K., Roy, S. (eds) Proceedings of 6th International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 177. Springer, Singapore. https://doi.org/10.1007/978-981-33-4501-0_11
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DOI: https://doi.org/10.1007/978-981-33-4501-0_11
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