Accesso libero

Iteration over Event Space in Time-to-First-Spike Spiking Neural Networks for Twitter (X) Bot Classification

,  e   
08 set 2025
INFORMAZIONI SU QUESTO ARTICOLO

Cita
Scarica la copertina

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Huh, D. and Sejnowski, T.J. (2018). Gradient descent for spiking neural networks, Advances in Neural Information Processing Systems 31: 1440–1450. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Lee, J.H., Delbruck, T. and Pfeiffer, M. (2016). Training deep spiking neural networks using backpropagation, Frontiers in Neuroscience 10: 508. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Nunes, J.D., Carvalho, M., Carneiro, D. and Cardoso, J.S. (2022). Spiking neural networks: A survey, IEEE Access 10: 60738–60764. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Pfeiffer, M. and Pfeil, T. (2018). Deep learning with spiking neurons: Opportunities and challenges, Frontiers in Neuroscience 12: 774. Search in Google Scholar

Rasmussen, D. (2019). NengoDL: Combining deep learning and neuromorphic modelling methods, Neuroinformatics 17(4): 611–628. Search in Google Scholar

Rodríguez-Ruiz, J., Mata-Sánchez, J.I., Monroy, R., Loyola-Gonzalez, O. and López-Cuevas, A. (2020). A one-class classification approach for bot detection on Twitter, Computers & Security 91: 101715. Search in Google Scholar

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. Search in Google Scholar

Shrestha, S.B. and Orchard, G. (2018). SLAYER: Spike layer error reassignment in time, Advances in Neural Information Processing Systems 31: 1419–1428. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Werbos, P.J. (1990). Backpropagation through time: What it does and how to do it, Proceedings of the IEEE 78(10): 1550–1560. Search in Google Scholar

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. Search in Google Scholar

Wunderlich, T.C. and Pehle, C. (2021). Event-based backpropagation can compute exact gradients for spiking neural networks, Scientific Reports 11(1): 12829. Search in Google Scholar

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. Search in Google Scholar

Zenke, F. and Ganguli, S. (2018). SuperSpike: Supervised learning in multilayer spiking neural networks, Neural Computation 30(6): 1514–1541. Search in Google Scholar

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. Search in Google Scholar

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). Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Matematica, Matematica applicata