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There has been an increasing interest in inferring future links on temporal knowledge graphs (KG).
Recurrent event network: Global structure inference over temporal knowledge graph
Woojeong Jin, He Jiang, Meng Qu, Tong Chen, Changlin Zhang, Pedro Szekely, and Xiang Ren. 2019 · 1904
Earlier work this paper cites.
Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019 · 1911
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The extension of factor analysis to three-dimensional matrices
L. R. Tucker. 1964 · 1964
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Interpolated adjoint method for neural odes
Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, and Ivan Oseledets. 2020 · 2003
Earlier work this paper cites.
The graph hawkes network for reasoning on temporal knowledge graphs
Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Guünnemann, and Volker Tresp. 2020b · 2003
Earlier work this paper cites.
Barycentric lagrange interpolation
Jean-Paul Berrut and Lloyd N Trefethen. 2004 · 2004
Earlier work this paper cites.
Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion
Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. 2020a · 2011
Earlier work this paper cites.
Temporal graph modeling for skeleton-based action recognition
Jianan Li, Xuemei Xie, Zhifu Zhao, Yuhan Cao, Qingzhe Pan, and Guangming Shi. 2020 · 2012
Earlier work this paper cites.
A brief introduction to numerical analysis
Eugene E Tyrtyshnikov. 2012 · 2012
Earlier work this paper cites.
Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks
Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhan. 2020 · 2012
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Durán, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Biega, and Fabian M Suchanek. 2013 · 2013
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014 · 2014
Cited alongside, same era.
ICEWS Coded Event Data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. 2015 · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Learning sequence encoders for temporal knowledge graph completion
Alberto García-Durán, Sebastijan Dumančić, and Mathias Niepert. 2018 · 2018
Later among the works it cites.
Deriving validity time in knowledge graph
Julien Leblay and Melisachew Wudage Chekol. 2018 · 2018
Later among the works it cites.
Spatial temporal graph convolutional networks for skeleton-based action recognition
Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018 · 2018
Later among the works it cites.
TuckER: Tensor factorization for knowledge graph completion
Ivana Balazevic, Carl Allen, and Timothy Hospedales. 2019 · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Later among the works it cites.
Dynamic knowledge graph based multi-event forecasting
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Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2017 · 2017
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2017 · 2017
Cited alongside, same era.
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017 · 2017
Cited alongside, same era.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018 · 2018
Cited alongside, same era.
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
Songgaojun Deng, Huzefa Rangwala, and Yue Ning. 2020 · 2020
Later among the works it cites.
Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 2020 · 2020
Later among the works it cites.
Tensor decompositions for temporal knowledge base completion
Timothee Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020 · 2020
Later among the works it cites.
Beta embeddings for multi-hop logical reasoning in knowledge graphs
Hongyu Ren and Jure Leskovec. 2020 · 2020
Later among the works it cites.
xerte: Explainable reasoning on temporal knowledge graphs for forecasting future links
Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp. 2021 · 2021
Closest in time.
Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016 · 2023
Closest in time.