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Continual learning seeks to empower models to progressively acquire information from a sequence of tasks.
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Arthur Gretton, Alex Smola, Jiayuan Huang, Marcel Schmittfull, Karsten Borgwardt, and Bernhard Schölkopf · 2009
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Impossibility theorems for domain adaptation
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Less-forgetting learning in deep neural networks
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
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Contextual stochastic block models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, and Elchanan Mossel · 2018
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Inductive representation learning on large graphs, 2018
William L. Hamilton, Rex Ying, and Jure Leskovec · 2018
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Continuous-time dynamic network embeddings
Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim · 2018
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Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks
Wenchao Yu, Wei Cheng, Charu C Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang · 2018
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Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Scalable and order-robust continual learning with additive parameter decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung Ju Hwang · 2019
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Structure aware experience replay for incremental learning in graph-based recommender systems
Kian Ahrabian, Yishi Xu, Yingxue Zhang, Jiapeng Wu, Yuening Wang, and Mark Coates · 2021
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Catastrophic forgetting in deep graph networks: an introductory benchmark for graph classification
Antonio Carta, Andrea Cossu, Federico Errica, and Davide Bacciu · 2021
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Continual learning of knowledge graph embeddings
Angel Daruna, Mehul Gupta, Mohan Sridharan, and Sonia Chernova · 2021
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A continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Lifelong learning of graph neural networks for open-world node classification
Lukas Galke, Benedikt Franke, Tobias Zielke, and Ansgar Scherp · 2021
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Magdalena Biesialska, Katarzyna Biesialska, and Marta R Costa-jussà · 2020
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Online learned continual compression with adaptive quantization modules
Lucas Caccia, Eugene Belilovsky, Massimo Caccia, and Joelle Pineau · 2020
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Online continual learning from imbalanced data
Aristotelis Chrysakis and Marie-Francine Moens · 2020
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Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
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Incremental learning on growing graphs
Yutong Feng, Jianwen Jiang, and Yue Gao · 2020
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Graph neural networks with continual learning for fake news detection from social media
Yi Han, Shanika Karunasekera, and Christopher Leckie · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Overcoming catastrophic forgetting in graph neural networks
Huihui Liu, Yiding Yang, and Xinchao Wang · 2021
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Subgroup generalization and fairness of graph neural networks, 2021
Jiaqi Ma, Junwei Deng, and Qiaozhu Mei · 2021
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2021
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Hierarchical prototype networks for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2021
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A theory for knowledge transfer in continual learning
Diana Benavides-Prado and Patricia Riddle · 2022
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Fildne: a framework for incremental learning of dynamic networks embeddings
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, and Nitesh V Chawla · 2022
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Multimodal continual graph learning with neural architecture search
Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, and Wenwu Zhu · 2022
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Dygrain: An incremental learning framework for dynamic graphs
Seoyoon Kim, Seongjun Yun, and Jaewoo Kang · 2022
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Graph few-shot class-incremental learning
Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu · 2022
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Lifelong graph learning
Chen Wang, Yuheng Qiu, Dasong Gao, and Sebastian Scherer · 2022
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Cglb: Benchmark tasks for continual graph learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2022
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Graph lifelong learning: A survey
Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, and Charu Aggarwal · 2023
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Towards robust graph incremental learning on evolving graphs
Junwei Su, Difan Zou, Zijun Zhang, and Chuan Wu · 2023
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Continual graph learning: A survey
Qiao Yuan, Sheng-Uei Guan, Pin Ni, Tianlun Luo, Ka Lok Man, Prudence Wong, and Victor Chang · 2023
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