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Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task.
Evolvegcn: Evolving graph convolutional networks for dynamic graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, and Charles E Leisersen. 2019 · 1902
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Relational graph attention networks
Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y Hammerla. 2019 · 1904
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Recurrent event network for reasoning over temporal knowledge graphs
Woojeong Jin, Changlin Zhang, Pedro Szekely, and Xiang Ren. 2019 · 1904
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Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 2019 · 1907
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Variational graph recurrent neural networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, and Xiaoning Qian. 2019 · 1908
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Deep graph library: Towards efficient and scalable deep learning on graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, et al. 2019 · 1909
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Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019 · 1911
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Temporal knowledge graph embedding model based on additive time series decomposition
Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Jens Lehmann, and Hamed Shariat Yazdi. 2019 · 1911
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Temporal attribute prediction via joint modeling of multi-relational structure evolution
Sankalp Garg, Navodita Sharma, Woojeong Jin, and Xiang Ren. 2020 · 2003
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The graph hawkes network for reasoning on temporal knowledge graphs
Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Guünnemann, and Volker Tresp. 2020 · 2003
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Tensor decompositions for temporal knowledge base completion
Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020 · 2004
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Hyte: Hyperplane-based temporally aware knowledge graph embedding
Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. 2018 · 2011
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011 · 2011
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
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Gdelt: Global data on events, location, and tone
Kalev Leetaru and Philip A Schrodt. 2013 · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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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
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ICEWS Coded Event Data
Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shellman, James Starz, and Michael Ward. 2015 · 2015
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Combining temporal aspects of dynamic networks with node2vec for a more efficient dynamic link prediction
Sam De Winter, Tim Decuypere, Sandra Mitrović, Bart Baesens, and Jochen De Weerdt. 2018 · 2018
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Learning sequence encoders for temporal knowledge graph completion
Alberto García-Durán, Sebastijan Dumančić, and Mathias Niepert. 2018 · 2018
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Tequila: Temporal question answering over knowledge bases
Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Strötgen, and Gerhard Weikum. 2018 · 2018
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Simple embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi and David Poole. 2018 · 2018
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Learning dynamic embeddings from temporal interactions
Srijan Kumar, Xikun Zhang, and Jure Leskovec. 2018 · 2018
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Towards time-aware knowledge graph completion
Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, and Zhifang Sui. 2016 · 2016
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Stranse: a novel embedding model of entities and relationships in knowledge bases
Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, and Mark Johnson. 2016 · 2016
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Holographic embeddings of knowledge graphs
Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio. 2016 · 2016
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Representation learning on graphs: Methods and applications
W. Hamilton, R. Ying, and J. Leskovec. 2017 · 2017
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Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Recurrent neural networks for multivariate time series with missing values
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018 · 2018
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Deriving validity time in knowledge graph
Julien Leblay and Melisachew Wudage Chekol. 2018 · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
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Dynamic network embedding by modeling triadic closure process
Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018 · 2018
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019 · 2019
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Dyrep: Learning representations over dynamic graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019 · 2019
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Dynamic graph convolutional networks
Franco Manessi, Alessandro Rozza, and Mario Manzo. 2020 · 2020
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Dysat: Deep neural representation learning on dynamic graphs via self-attention networks
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2020 · 2020
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
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