1. Aggarwal, C.C.: Machine Learning for Text. Springer, Cham (2018).
https://doi.org/10.1007/978-3-319-73531-3
2. Androutsopoulos, I., Koutsias, J., Chandrinos, K., Paliouras, G., Spyropoulos, C.: An evaluation of Naïve Bayesian anti-spam filtering. In: Proceedings of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning, pp. 9–17 (2000)
3. Fakhraei, S., Foulds, J., Shashanka, M., Getoor, L.: Collective spammer detection in evolving multi-relational social networks. In: Proceedings of the 21 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1769–1778 (2015)
4. Giarelis, N., Kanakaris, N., Karacapilidis, N.: On a novel representation of multiple textual documents in a single graph. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2020 – Proceedings of the 12th KES International Conference on Intelligent Decision Technologies (KES-IDT-20), Split, Croatia, 17–19 June 2020. Springer (2020)
5. Henni, K., Mezghani, N., Gouin-Vallerand, C.: Unsupervised graph-based feature selection via subspace and PageRank centrality. Expert Syst. Appl. 114, 46–53 (2018)