暂无分享,去创建一个
[1] Joshua B. Tenenbaum,et al. Separating Style and Content with Bilinear Models , 2000, Neural Computation.
[2] Bruno Ribeiro,et al. Subgraph Pattern Neural Networks for High-Order Graph Evolution Prediction , 2018, AAAI.
[3] Jure Leskovec,et al. Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.
[4] Aynaz Taheri,et al. Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models , 2019, WWW.
[5] Chen Sun,et al. Stochastic Prediction of Multi-Agent Interactions from Partial Observations , 2019, ICLR.
[6] F. Schoenberg. Introduction to Point Processes , 2011 .
[7] Corrado Loglisci,et al. Leveraging temporal autocorrelation of historical data for improving accuracy in network regression , 2017, Stat. Anal. Data Min..
[8] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[9] Jure Leskovec,et al. Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..
[10] Partha Talukdar,et al. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding , 2018, EMNLP.
[11] Silvio Savarese,et al. Structural-RNN: Deep Learning on Spatio-Temporal Graphs , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Hongyuan Zha,et al. DyRep: Learning Representations over Dynamic Graphs , 2019, ICLR.
[13] Richard S. Zemel,et al. Gated Graph Sequence Neural Networks , 2015, ICLR.
[14] Wei Zhang,et al. Dynamic Graph Representation Learning via Self-Attention Networks , 2018, ArXiv.
[15] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[16] Abhinav Gupta,et al. Temporal Dynamic Graph LSTM for Action-Driven Video Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[17] Yan Liu,et al. DynGEM: Deep Embedding Method for Dynamic Graphs , 2018, ArXiv.
[18] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[19] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[20] Palash Goyal,et al. dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning , 2018, Knowl. Based Syst..
[21] Pascal Poupart,et al. Diachronic Embedding for Temporal Knowledge Graph Completion , 2019, AAAI.
[22] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[23] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[24] Alex Pentland,et al. Sensing the "Health State" of a Community , 2012, IEEE Pervasive Computing.
[25] Pascal Poupart,et al. Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey , 2019, ArXiv.
[26] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[27] Shweta Bansal,et al. The dynamic nature of contact networks in infectious disease epidemiology , 2010, Journal of biological dynamics.
[28] Jinyin Chen,et al. GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction , 2018, Applied Intelligence.
[29] Chen Sun,et al. Unsupervised Discovery of Parts, Structure, and Dynamics , 2019, ICLR.
[30] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[31] Volker Tresp,et al. Embedding models for episodic knowledge graphs , 2018, J. Web Semant..
[32] Zhifang Sui,et al. Towards Time-Aware Knowledge Graph Completion , 2016, COLING.
[33] Razvan Pascanu,et al. A simple neural network module for relational reasoning , 2017, NIPS.
[34] Stephan Günnemann,et al. Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs , 2020, ArXiv.
[35] Jason Eisner,et al. The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process , 2016, NIPS.
[36] O. Aalen,et al. Survival and Event History Analysis: A Process Point of View , 2008 .
[37] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[38] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[39] Max Welling,et al. Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.
[40] R. Zemel,et al. Neural Relational Inference for Interacting Systems , 2018, ICML.
[41] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[42] Yejin Choi,et al. Neural Motifs: Scene Graph Parsing with Global Context , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[43] Chen Sun,et al. Unsupervised Learning of Object Structure and Dynamics from Videos , 2019, NeurIPS.
[44] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[45] Le Song,et al. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs , 2017, ICML.
[46] Purnamrita Sarkar,et al. A Latent Space Approach to Dynamic Embedding of Co-occurrence Data , 2007, AISTATS.
[47] Mathias Niepert,et al. Learning Sequence Encoders for Temporal Knowledge Graph Completion , 2018, EMNLP.
[48] Yueting Zhuang,et al. Dynamic Network Embedding by Modeling Triadic Closure Process , 2018, AAAI.
[49] Ryan A. Rossi,et al. Continuous-Time Dynamic Network Embeddings , 2018, WWW.
[50] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[51] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.