Abstract
One of the interesting tasks in social network analysis is detecting network nodes’ roles in their interactions. The first problem is discovering such roles, and the second is detecting the discovered roles in the network. Role detection, i.e., assigning a role to a node, is a classification task. Our paper addresses the second problem and uses three roles (classes) for classification. These roles are based only on the structural properties of the neighborhood of a given node and use the previously published non-symmetric relationship between pairs of nodes for their definition. This paper presents transductive learning experiments using graph neural networks (GNN) to show that excellent results can be obtained even with a relatively small sample size for training the network.
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Acknowledgements
This work is partially supported by SGS, VSB-Technical University of Ostrava, under the grant no. SP2022/77 and Ministry of Health of the Czech Republic under grants no. NU20-06-00269, NU21-06-00370.
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Dopater, E., Kudělka, M. (2023). Node Classification Based on Non-symmetric Dependencies and Graph Neural Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_27
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