Iteration over Event Space in Time-to-First-Spike Spiking Neural Networks for Twitter (X) Bot Classification
By: Mateusz Pabian, Dominik Rzepka and MirosĹ‚aw Pawlak Â
References
- Balaanand, M., Karthikeyan, N., Karthik, S., Varatharajan, R., Manogaran, G. and Sivaparthipan, C.B. (2019). An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter, Journal of Supercomputing 75(9): 6085–6105.
- Bohte, S.M., Kok, J.N. and La Poutre, H. (2002). Error-backpropagation in temporally encoded networks of spiking neurons, Neurocomputing 48(1–4): 17–37.
- Cao, Q., Sirivianos, M., Yang, X. and Pregueiro, T. (2012). Aiding the detection of fake accounts in large scale social online services, 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12), San Jose, USA, pp. 197–210.
- Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A. and Tesconi, M. (2017). The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race, Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, pp. 963–972.
- Davis, C.A., Varol, O., Ferrara, E., Flammini, A. and Menczer, F. (2016). BotOrNot: A system to evaluate social bots, Proceedings of the 25th International Conference Companion on World Wide Web, Quebec, Canada, pp. 273–274.
- Dutta, H.S., Chetan, A., Joshi, B. and Chakraborty, T. (2018). Retweet us, we will retweet you: Spotting collusive retweeters involved in blackmarket services, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, pp. 242–249.
- Eshraghian, J.K., Ward, M., Neftci, E.O., Wang, X., Lenz, G., Dwivedi, G., Bennamoun, M., Jeong, D.S. and Lu, W.D. (2023). Training spiking neural networks using lessons from deep learning, Proceedings of the IEEE 111(9): 1016–1054.
- Falez, P., Tirilly, P., Bilasco, I.M., Devienne, P. and Boulet, P. (2019). Unsupervised visual feature learning with spike-timing-dependent plasticity: How far are we from traditional feature learning approaches?, Pattern Recognition 93: 418–429.
- Fang, W., Yu, Z., Chen, Y., Huang, T., Masquelier, T. and Tian, Y. (2021). Deep residual learning in spiking neural networks, Advances in Neural Information Processing Systems 34: 21056–21069.
- Ferrara, E., Varol, O., Davis, C., Menczer, F. and Flammini, A. (2016). The rise of social bots, Communications of the ACM 59(7): 96–104.
- Huh, D. and Sejnowski, T.J. (2018). Gradient descent for spiking neural networks, Advances in Neural Information Processing Systems 31: 1440–1450.
- Javanshir, A., Nguyen, T.T.,Mahmud, M.A.P. and Kouzani, A.Z. (2022). Advancements in algorithms and neuromorphic hardware for spiking neural networks, Neural Computation 34(6): 1289–1328.
- Kheradpisheh, S.R. and Masquelier, T. (2020). Temporal backpropagation for spiking neural networks with one spike per neuron, International Journal of Neural Systems 30(06): 2050027.
- Kim, Y., Chough, J. and Panda, P. (2022). Beyond classification: Directly training spiking neural networks for semantic segmentation, Neuromorphic Computing and Engineering 2(4): 044015.
- Lee, J.H., Delbruck, T. and Pfeiffer, M. (2016). Training deep spiking neural networks using backpropagation, Frontiers in Neuroscience 10: 508.
- Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W. and Tesconi, M. (2019). RTbust: Exploiting temporal patterns for botnet detection on Twitter, Proceedings of the 10th ACM Conference on Web Science, Boston, USA, pp. 183–192.
- Midya, R., Wang, Z., Asapu, S., Joshi, S., Li, Y., Zhuo, Y., Song, W., Jiang, H., Upadhay, N., Rao, M., Lin, P., Li, C., Xia, Q. and Yang, J.J. (2019). Artificial neural network (ANN) to spiking neural network (SNN) converters based on diffusive memristors, Advanced Electronic Materials 5(9): 1900060.
- Minnich, A., Chavoshi, N., Koutra, D. and Mueen, A. (2017). BotWalk: Efficient adaptive exploration of Twitter bot networks, Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia, pp. 467–474.
- Mostafa, H. (2017). Supervised learning based on temporal coding in spiking neural networks, IEEE Transactions on Neural Networks and Learning Systems 29(7): 3227–3235.
- Mozafari, M., Ganjtabesh, M., Nowzari-Dalini, A., Thorpe, S.J. and Masquelier, T. (2019). Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks, Pattern Recognition 94: 87–95.
- Neftci, E.O., Mostafa, H. and Zenke, F. (2019). Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks, IEEE Signal Processing Magazine 36(6): 51–63.
- Nunes, J.D., Carvalho, M., Carneiro, D. and Cardoso, J.S. (2022). Spiking neural networks: A survey, IEEE Access 10: 60738–60764.
- Pabian, M., Rzepka, D. and Pawlak, M. (2022). Supervised training of siamese spiking neural networks with earth mover’s distance, ICASSP 22022 IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, pp. 4233–4237.
- Pan, J., Liu, Y., Liu, X. and Hu, H. (2016). Discriminating bot accounts based solely on temporal features of microblog behavior, Physica A: Statistical Mechanics and its Applications 450(C): 193–204.
- Pfeiffer, M. and Pfeil, T. (2018). Deep learning with spiking neurons: Opportunities and challenges, Frontiers in Neuroscience 12: 774.
- Rasmussen, D. (2019). NengoDL: Combining deep learning and neuromorphic modelling methods, Neuroinformatics 17(4): 611–628.
- RodrĂguez-Ruiz, J., Mata-Sánchez, J.I., Monroy, R., Loyola-Gonzalez, O. and LĂłpez-Cuevas, A. (2020). A one-class classification approach for bot detection on Twitter, Computers & Security 91: 101715.
- Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M. and Liu, S.-C. (2017). Conversion of continuous-valued deep networks to efficient event-driven networks for image classification, Frontiers in Neuroscience 11: 682.
- Shrestha, S.B. and Orchard, G. (2018). SLAYER: Spike layer error reassignment in time, Advances in Neural Information Processing Systems 31: 1419–1428.
- Stöckl, C. and Maass, W. (2021). Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes, Nature Machine Intelligence 3(3): 230–238.
- Thorpe, S. and Gautrais, J. (1998). Rank order coding, 6th Annual Conference on Computational Neuroscience: Trends in Research, 1998, Big Sky, USA, pp. 113–118.
- Werbos, P.J. (1990). Backpropagation through time: What it does and how to do it, Proceedings of the IEEE 78(10): 1550–1560.
- Wu, Y., Deng, L., Li, G., Zhu, J. and Shi, L. (2018). Spatio-temporal backpropagation for training high-performance spiking neural networks, Frontiers in Neuroscience 12: 331.
- Wunderlich, T.C. and Pehle, C. (2021). Event-based backpropagation can compute exact gradients for spiking neural networks, Scientific Reports 11(1): 12829.
- Yin, B., Corradi, F. and Bohté, S.M. (2021). Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks, Nature Machine Intelligence 3(10): 905–913.
- Zenke, F. and Ganguli, S. (2018). SuperSpike: Supervised learning in multilayer spiking neural networks, Neural Computation 30(6): 1514–1541.
- Zhang, W. and Li, P. (2020). Temporal spike sequence learning via backpropagation for deep spiking neural networks, Advances in Neural Information Processing Systems 33: 12022–12033.
- Zheng, H., Wu, Y., Deng, L., Hu, Y. and Li, G. (2021). Going deeper with directly-trained larger spiking neural networks, Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11062–11070, (virtual event).
- Zhou, S., Li, X., Chen, Y., Chandrasekaran, S.T. and Sanyal, A. (2021). Temporal-coded deep spiking neural network with easy training and robust performance, Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11143–11151.
- Zhu, Y., Yu, Z., Fang, W., Xie, X., Huang, T. and Masquelier, T. (2022). Training spiking neural networks with event-driven backpropagation, Advances in Neural Information Processing Systems 35: 30528–30541.
DOI:Â https://doi.org/10.61822/amcs-2025-0035 | Journal eISSN:Â 2083-8492 | Journal ISSN:Â 1641-876X
Language:Â English
Page range:Â 493 - 505
Submitted on:Â Sep 29, 2024
Accepted on:Â Apr 14, 2025
Published on:Â Sep 8, 2025
Published by:Â University of Zielona GĂłra
In partnership with:Â Paradigm Publishing Services
Publication frequency:Â 4 issues per year
Related subjects:
© 2025 Mateusz Pabian, Dominik Rzepka, Mirosław Pawlak, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.