Deep graph kernel point processes
暂无分享,去创建一个
[1] Yuchen Li,et al. Graph Neural Point Process for Temporal Interaction Prediction , 2023, IEEE Transactions on Knowledge and Data Engineering.
[2] M. Bianchini,et al. Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities , 2023, ArXiv.
[3] Xiuyuan Cheng,et al. Spatio-temporal point processes with deep non-stationary kernels , 2022, ICLR.
[4] Jianheng Tang,et al. Rethinking Graph Neural Networks for Anomaly Detection , 2022, ICML.
[5] Vijay Prakash Dwivedi,et al. Recipe for a General, Powerful, Scalable Graph Transformer , 2022, NeurIPS.
[6] Mihai Cucuringu,et al. Graph similarity learning for change-point detection in dynamic networks , 2022, Mach. Learn..
[7] Jason Eisner,et al. Transformer Embeddings of Irregularly Spaced Events and Their Participants , 2021, ICLR.
[8] Eran Yahav,et al. How Attentive are Graph Attention Networks? , 2021, ICLR.
[9] Hisashi Kashima,et al. Dynamic Hawkes Processes for Discovering Time-evolving Communities' States behind Diffusion Processes , 2021, KDD.
[10] Bryan Hooi,et al. Graph Neural Network-Based Anomaly Detection in Multivariate Time Series , 2021, AAAI.
[11] Emine Yilmaz,et al. Learning Neural Point Processes with Latent Graphs , 2021, WWW.
[12] Shuang Li,et al. Imitation Learning of Neural Spatio-Temporal Point Processes , 2021, IEEE Transactions on Knowledge and Data Engineering.
[13] Xavier Bresson,et al. A Generalization of Transformer Networks to Graphs , 2020, ArXiv.
[14] Qiang Qiu,et al. Graph Convolution with Low-rank Learnable Local Filters , 2020, ICLR.
[15] Emine Yilmaz,et al. Self-Attentive Hawkes Process , 2020, ICML.
[16] Haifeng Chen,et al. Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs , 2020, CIKM.
[17] Biao Cai,et al. Latent Network Structure Learning from High Dimensional Multivariate Point Processes , 2020, Journal of the American Statistical Association.
[18] Yao Xie,et al. Convex Parameter Recovery for Interacting Marked Processes , 2020, IEEE Journal on Selected Areas in Information Theory.
[19] Hongyuan Zha,et al. Transformer Hawkes Process , 2020, ICML.
[20] Stephan Günnemann,et al. Intensity-Free Learning of Temporal Point Processes , 2019, ICLR.
[21] Hongyuan Zha,et al. Modeling Event Propagation via Graph Biased Temporal Point Process , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[22] Mingxuan Sun,et al. Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks , 2019, AAAI.
[23] K. Aihara,et al. Fully Neural Network based Model for General Temporal Point Processes , 2019, NeurIPS.
[24] Shixiang Zhu,et al. Spatial-Temporal-Textual Point Processes for Crime Linkage Detection , 2019, 1902.00440.
[25] Lorenzo Livi,et al. Graph Neural Networks With Convolutional ARMA Filters , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Georgios B. Giannakis,et al. A Recurrent Graph Neural Network for Multi-relational Data , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[27] Stefan Klus,et al. Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces , 2018, Advances in Dynamics, Optimization and Computation.
[28] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[29] Alex Reinhart,et al. A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications , 2017, Statistical Science.
[30] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[31] Jason Eisner,et al. The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process , 2016, NIPS.
[32] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[33] Utkarsh Upadhyay,et al. Recurrent Marked Temporal Point Processes: Embedding Event History to Vector , 2016, KDD.
[34] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[35] Donald F. Towsley,et al. Diffusion-Convolutional Neural Networks , 2015, NIPS.
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Scott W. Linderman,et al. Discovering Latent Network Structure in Point Process Data , 2014, ICML.
[38] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[39] Esko Valkeila,et al. An Introduction to the Theory of Point Processes, Volume II: General Theory and Structure, 2nd Edition by Daryl J. Daley, David Vere‐Jones , 2008 .
[40] Yosihiko Ogata,et al. Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes , 1988 .
[41] A. Hawkes. Spectra of some self-exciting and mutually exciting point processes , 1971 .
[42] Junchi Yan,et al. Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph , 2021, IJCAI.
[43] Piotr Koniusz,et al. Simple Spectral Graph Convolution , 2021, ICLR.
[44] Yiming Yang,et al. Correlation-Aware Change-Point Detection via Graph Neural Networks , 2020, ICONIP.
[45] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[46] Tian Gao,et al. Proximal Graphical Event Models , 2018, NeurIPS.
[47] HighWire Press. Philosophical transactions of the Royal Society of London. Series A, Containing papers of a mathematical or physical character , 1896 .