| A brief review of hypernetworks in deep learning VK Chauhan, J Zhou, P Lu, S Molaei, DA Clifton Artificial Intelligence Review 57 (9), 250, 2024 | 109 | 2024 |
| Deep learning approach on information diffusion in heterogeneous networks S Molaei, H Zare, H Veisi Knowledge-Based Systems 189, 105153, 2020 | 58 | 2020 |
| Identifying influential nodes in heterogeneous networks S Molaei, R Farahbakhsh, M Salehi, N Crespi Expert Systems with Applications 160, 113580, 2020 | 37 | 2020 |
| Collaborative deep forest learning for recommender systems S Molaei, A Havvaei, H Zare, M Jalili IEEE Access 9, 22053-22061, 2021 | 24 | 2021 |
| Information spread and topic diffusion in heterogeneous information networks S Molaei, S Babaei, M Salehi, M Jalili Scientific reports 8 (1), 9549, 2018 | 24 | 2018 |
| Learning Graph Representations With Maximal Cliques S Molaei, N Ghanbari Bousejin, H Zare, M Jalili, S Pan IEEE Trans Neural Netw Learn Syst, 2021 | 20 | 2021 |
| Predicting the spread of influenza epidemics by analyzing twitter messages S Molaei, M Khansari, H Veisi, M Salehi Health and Technology 9, 517-532, 2019 | 20 | 2019 |
| Adversarial de-confounding in individualised treatment effects estimation V Kumar, S Molaei, MH Tania, A Thakur, T Zhu, D Clifton Artificial Intelligence and Statistics (AISTATS), 2022 | 18* | 2022 |
| Deep node clustering based on mutual information maximization S Molaei, N Ghanbari Bousejin, H Zare, M Jalili Neurocomputing 455, 274-282, 2021 | 15 | 2021 |
| GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels X Zheng, M Zhang, C Chen, S Molaei, C Zhou, S Pan Advances in Neural Information Processing Systems (NeurIPS), 2023 | 14 | 2023 |
| Graph representation learning based on deep generative gaussian mixture models G Niknam, S Molaei, H Zare, D Clifton, S Pan Neurocomputing 523, 157-169, 2023 | 14 | 2023 |
| GCNFusion: An efficient graph convolutional network based model for information diffusion B Fatemi, S Molaei, S Pan, SA Rahimi Expert Systems with Applications 202, 117053, 2022 | 14 | 2022 |
| Dynamic inter-treatment information sharing for individualized treatment effects estimation VK Chauhan, J Zhou, G Ghosheh, S Molaei, DA Clifton International Conference on Artificial Intelligence and Statistics, 3529-3537, 2024 | 13* | 2024 |
| DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks G Niknam, S Molaei, H Zare, S Pan, M Jalili, T Zhu, D Clifton Neural Networks, 2023 | 8 | 2023 |
| Classification of audio codecs with variable bit-rates using deep-learning methods A Khodadadi, S Molaei, M Teimouri, H Zare Digital Signal Processing 110, 102952, 2021 | 8 | 2021 |
| Federated learning for heterogeneous electronic health records utilising augmented temporal graph attention networks S Molaei, A Thakur, G Niknam, A Soltan, H Zare, DA Clifton International Conference on Artificial Intelligence and Statistics, 1342-1350, 2024 | 7 | 2024 |
| Improving diagnostics with deep forest applied to electronic health records A Khodadadi, N Ghanbari Bousejin, S Molaei, V Kumar Chauhan, T Zhu, ... Sensors 23 (14), 6571, 2023 | 6 | 2023 |
| Continuous patient state attention model for addressing irregularity in electronic health records VK Chauhan, A Thakur, O O’Donoghue, O Rohanian, S Molaei, DA Clifton BMC Medical Informatics and Decision Making 24 (1), 117, 2024 | 3 | 2024 |
| Attributed graph clustering via deep adaptive graph maximization B Fatemi, S Molaei, H Zare, H Veisi 2020 10th International Conference on Computer and Knowledge Engineering …, 2020 | 2 | 2020 |
| Modeling information diffusion in bibliographic multilayer networks S Babaei, S Molaei, M Salehi TABRIZ JOURNAL OF ELECTRICAL ENGINEERING 49, 2019 | 2 | 2019 |
David A. CliftonChair of Clinical Machine Learning, University of OxfordVerified email at eng.ox.ac.uk
