| Identifying Human Mobility via Trajectory Embeddings. Q Gao, F Zhou, K Zhang, G Trajcevski, X Luo, F Zhang IJCAI 17, 1689-1695, 2017 | 192 | 2017 |
| Meta-gnn: On few-shot node classification in graph meta-learning F Zhou, C Cao, K Zhang, G Trajcevski, T Zhong, J Geng Proceedings of the 28th ACM International Conference on Information and …, 2019 | 186 | 2019 |
| Deeplink: A deep learning approach for user identity linkage F Zhou, L Liu, K Zhang, G Trajcevski, J Wu, T Zhong IEEE INFOCOM 2018-IEEE Conference on computer communications, 1313-1321, 2018 | 184 | 2018 |
| A survey of information cascade analysis: Models, predictions, and recent advances F Zhou, X Xu, G Trajcevski, K Zhang ACM Computing Surveys (CSUR) 54 (2), 1-36, 2021 | 147 | 2021 |
| Information diffusion prediction via recurrent cascades convolution X Chen, F Zhou, K Zhang, G Trajcevski, T Zhong, F Zhang 2019 IEEE 35th international conference on data engineering (ICDE), 770-781, 2019 | 147 | 2019 |
| Adversarial point-of-interest recommendation F Zhou, R Yin, K Zhang, G Trajcevski, T Zhong, J Wu The world wide web conference, 3462-34618, 2019 | 117 | 2019 |
| Predicting human mobility via variational attention Q Gao, F Zhou, G Trajcevski, K Zhang, T Zhong, F Zhang The world wide web conference, 2750-2756, 2019 | 114 | 2019 |
| Variational graph neural networks for road traffic prediction in intelligent transportation systems F Zhou, Q Yang, T Zhong, D Chen, N Zhang IEEE Transactions on Industrial Informatics 17 (4), 2802-2812, 2020 | 112 | 2020 |
| Trajectory-User Linking via Variational AutoEncoder. F Zhou, Q Gao, G Trajcevski, K Zhang, T Zhong, F Zhang IJCAI, 3212-3218, 2018 | 106 | 2018 |
| Overcoming catastrophic forgetting in graph neural networks with experience replay F Zhou, C Cao Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4714-4722, 2021 | 89 | 2021 |
| Trajectory big data: a review of key technologies in data processing 高强， 张凤荔， 王瑞锦， 周帆 Journal of Software 28 (4), 959-992, 2016 | 80 | 2016 |
| Reinforced spatiotemporal attentive graph neural networks for traffic forecasting F Zhou, Q Yang, K Zhang, G Trajcevski, T Zhong, A Khokhar IEEE Internet of Things Journal 7 (7), 6414-6428, 2020 | 65 | 2020 |
| Hybrid graph convolutional networks with multi-head attention for location recommendation T Zhong, S Zhang, F Zhou, K Zhang, G Trajcevski, J Wu World Wide Web 23, 3125-3151, 2020 | 57 | 2020 |
| Rumor detection on social media with event augmentations Z He, C Li, F Zhou, Y Yang Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 55 | 2021 |
| DeepSeg: Deep-learning-based activity segmentation framework for activity recognition using WiFi C Xiao, Y Lei, Y Ma, F Zhou, Z Qin IEEE Internet of Things Journal 8 (7), 5669-5681, 2020 | 44 | 2020 |
| Information cascades modeling via deep multi-task learning X Chen, K Zhang, F Zhou, G Trajcevski, T Zhong, F Zhang Proceedings of the 42nd international ACM SIGIR conference on research and …, 2019 | 44 | 2019 |
| Variational information diffusion for probabilistic cascades prediction F Zhou, X Xu, K Zhang, G Trajcevski, T Zhong IEEE INFOCOM 2020-IEEE conference on computer communications, 1618-1627, 2020 | 41 | 2020 |
| Multi-view learning with distinguishable feature fusion for rumor detection X Chen, F Zhou, G Trajcevski, M Bonsangue Knowledge-Based Systems 240, 108085, 2022 | 40 | 2022 |
| Urban flow prediction with spatial–temporal neural ODEs F Zhou, L Li, K Zhang, G Trajcevski Transportation Research Part C: Emerging Technologies 124, 102912, 2021 | 40 | 2021 |
| Learning latent seasonal-trend representations for time series forecasting Z Wang, X Xu, W Zhang, G Trajcevski, T Zhong, F Zhou Advances in Neural Information Processing Systems 35, 38775-38787, 2022 | 39 | 2022 |
Goce TrajcevskiDept. of ECpE, Iowa State UniversityVerified email at iastate.edu
