A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances F Zhou, X Xu, G Trajcevski, K Zhang ACM Computing Surveys 54 (2), 27:1-27:36, 2021 | 161 | 2021 |
Learning Latent Seasonal-Trend Representations for Time Series Forecasting Z Wang, X Xu, G Trajcevski, W Zhang, T Zhong, F Zhou NeurIPS 35, 38775-38787, 2022 | 46 | 2022 |
Variational Information Diffusion for Probabilistic Cascades Prediction F Zhou, X Xu, K Zhang, G Trajcevski, T Zhong IEEE INFOCOM, 1618-1627, 2020 | 41 | 2020 |
CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction X Xu, F Zhou, K Zhang, S Liu, G Trajcevski IEEE TKDE 35 (4), 3484-3499, 2021 | 34 | 2021 |
CCGL: Contrastive Cascade Graph Learning X Xu, F Zhou, K Zhang, S Liu IEEE TKDE 35 (5), 4539-4554, 2022 | 33 | 2022 |
Contrastive Trajectory Learning for Tour Recommendation F Zhou, P Wang, X Xu, W Tai, G Trajcevski ACM TIST 13 (1), 4:1-4:25, 2022 | 27 | 2022 |
Transformer-Enhanced Hawkes Process With Decoupling Training for Information Cascade Prediction L Yu, X Xu, G Trajcevski, F Zhou Knowledge-Based Systems 255, 109740, 2022 | 16 | 2022 |
A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact F Zhou, X Xu, C Li, G Trajcevski, T Zhong, K Zhang arXiv:2003.12042, 8 pages, 2020 | 16 | 2020 |
Heterogeneous Dynamical Academic Network for Learning Scientific Impact Propagation X Xu, T Zhong, C Li, G Trajcevski, F Zhou Knowledge-Based Systems 238, 107839, 2022 | 14 | 2022 |
Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction F Zhou, L Yu, X Xu, G Trajcevski ACM SIGIR, 1875-1879, 2021 | 14 | 2021 |
Continual Information Cascade Learning F Zhou, X Jing, X Xu, T Zhong, G Trajcevski, J Wu IEEE GLOBECOM, 6 pages, 2020 | 13 | 2020 |
Counterfactual Graph Learning for Anomaly Detection on Attributed Networks C Xiao, X Xu, Y Lei, K Zhang, S Liu, F Zhou IEEE TKDE 35 (10), 10540-10553, 2023 | 11 | 2023 |
Unsupervised User Identity Linkage via Graph Neural Networks F Zhou, Z Wen, T Zhong, G Trajcevski, X Xu, L Liu IEEE GLOBECOM, 6 pages, 2020 | 10 | 2020 |
PrEF: Probabilistic Electricity Forecasting via Copula-Augmented State Space Model Z Wang, X Xu, G Trajcevski, K Zhang, T Zhong, F Zhou AAAI 36 (11), 12200-12207, 2022 | 9 | 2022 |
Overcoming Forgetting in Fine-Grained Urban Flow Inference via Adaptive Knowledge Replay H Yu, X Xu, T Zhong, F Zhou AAAI 37 (4), 5393-5401, 2023 | 8 | 2023 |
Diffusion Probabilistic Modeling for Fine-Grained Urban Traffic Flow Inference with Relaxed Structural Constraint X Xu, Y Wei, P Wang, X Luo, F Zhou, G Trajcevski IEEE ICASSP, 5 pages, 2023 | 5 | 2023 |
Probabilistic Fine-Grained Urban Flow Inference With Normalizing Flows T Zhong, H Yu, R Li, X Xu, X Luo, F Zhou IEEE ICASSP, 3663-3667, 2022 | 5 | 2022 |
PGSL: A Probabilistic Graph Diffusion Model for Source Localization X Xu, T Qian, Z Xiao, N Zhang, J Wu, F Zhou Expert Systems With Applications 238, 122028, 2024 | 4 | 2024 |
Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction X Xu, T Zhong, F Zhou, R Li, G Trajcevski, Q Meng IEEE Transactions on Cybernetics 54 (1), 572--585, 2024 | 4 | 2024 |
Fine-Grained Urban Flow Inference via Normalizing Flows (Student Abstract) H Yu, X Xu, T Zhong, F Zhou AAAI 36 (11), 13101-13102, 2022 | 4 | 2022 |