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Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks.
On tiny episodic memories in continual learning
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc’Aurelio Ranzato. 2019 · 1902
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen. 2019 · 1903
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Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019 · 1908
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Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I Weidele, Claudio Bellei, Tom Robinson, and Charles E Leiserson. 2019 · 1908
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020 · 2002
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Inductive representation learning on temporal graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020 · 2002
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2020 · 2003
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RDKit: Open-source cheminformatics
Greg Landrum et al · 2006
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Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020 · 2006
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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 · 2007
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Wiki-cs: A wikipedia-based benchmark for graph neural networks
Péter Mernyei and Cătălina Cangea. 2020 · 2007
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
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Arnetminer: extraction and mining of academic social networks. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 990–998
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. 2008 · 2008
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Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang. 2009 · 2009
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2010
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SLIC superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. 2012 · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio. 2013 · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
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Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs
Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada A Shahane, Anna Rossoshek, and Anton Simeonov. 2016 · 2015
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DeepTox: toxicity prediction using deep learning
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, and Sepp Hochreiter. 2016 · 2015
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In International Conference on World Wide Web . 507–517
Ruining He and Julian McAuley. 2016 · 2016
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A new view of the tree of life
Laura A Hug, Brett J Baker, Karthik Anantharaman, Christopher T Brown, Alexander J Probst, Cindy J Castelle, Cristina N Butterfield, Alex W Hernsdorf, Yuki Amano, Kotaro Ise, et al · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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Edge weight prediction in weighted signed networks. In IEEE International Conference on Data Mining . 221–230
Srijan Kumar, Francesca Spezzano, VS Subrahmanian, and Christos Faloutsos. 2016 · 2016
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Modeling user exposure in recommendation. In Proceedings of the 25th international conference on World Wide Web . 951–961
Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016 · 2016
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Transfer learning to infer social ties across heterogeneous networks
Jie Tang, Tiancheng Lou, Jon Kleinberg, and Sen Wu. 2016 · 2016
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann. 2017 · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 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 · 2017
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Core50: a new dataset and benchmark for continuous object recognition. In Conference on Robot Learning . 17–26
Vincenzo Lomonaco and Davide Maltoni. 2017 · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato. 2017 · 2017
Cited alongside, same era.
Motifs in temporal networks. In ACM International Conference on Web Search and Data Mining . 601–610
Ashwin Paranjape, Austin R Benson, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2001–2010
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. 2017 · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. 2017 · 2017
Cited alongside, same era.
Microsoft academic graph: When experts are not enough
Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia. 2020a · 2020
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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 · 2021
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Continual learning of knowledge graph embeddings
Angel Daruna, Mehul Gupta, Mohan Sridharan, and Sonia Chernova. 2021 · 2021
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Lifelong learning of graph neural networks for open-world node classification. In International Joint Conference on Neural Networks . IEEE, 1–8
Lukas Galke, Benedikt Franke, Tobias Zielke, and Ansgar Scherp. 2021 · 2021
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CLeaR: An adaptive continual learning framework for regression tasks
Yujiang He and Bernhard Sick. 2021 · 2021
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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget. In European Conference on Computer Vision . 139–154
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars. 2018 · 2018
Cited alongside, same era.
Riemannian walk for incremental learning: Understanding forgetting and intransigence. In European Conference on Computer Vision . 532–547
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr. 2018 · 2018
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik. 2018 · 2018
Cited alongside, same era.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira. 2018 · 2018
Cited alongside, same era.
Rev2: Fraudulent user prediction in rating platforms. In ACM International Conference on Web Search and Data Mining . 333–341
Srijan Kumar, Bryan Hooi, Disha Makhija, Mohit Kumar, Christos Faloutsos, and VS Subrahmanian. 2018 · 2018
Cited alongside, same era.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights. In European Conference on Computer Vision . 67–82
Arun Mallya, Dillon Davis, and Svetlana Lazebnik. 2018 · 2018
Cited alongside, same era.
Learning to Pool in Graph Neural Networks for Extrapolation
Jihoon Ko, Taehyung Kwon, Kijung Shin, and Juho Lee. 2021 · 2021
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Dgl-lifesci: An open-source toolkit for deep learning on graphs in life science
Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, and George Karypis. 2021 · 2021
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The clear benchmark: Continual learning on real-world imagery. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Zhiqiu Lin, Jia Shi, Deepak Pathak, and Deva Ramanan. 2021 · 2021
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Overcoming catastrophic forgetting in graph neural networks. In AAAI Conference on Artificial Intelligence , Vol. 35. 8653–8661
Huihui Liu, Yiding Yang, and Xinchao Wang. 2021 · 2021
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Avalanche: an end-to-end library for continual learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3600–3610
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M Van de Ven, et al · 2021
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Dualnet: Continual learning, fast and slow
Quang Pham, Chenghao Liu, and Steven Hoi. 2021 · 2021
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Benedek Rozemberczki and Rik Sarkar. 2021 · 2021
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Towards open-world feature extrapolation: An inductive graph learning approach
Qitian Wu, Chenxiao Yang, and Junchi Yan. 2021 · 2021
Later among the works it cites.
Overcoming catastrophic forgetting in graph neural networks with experience replay. In AAAI Conference on Artificial Intelligence , Vol. 35. 4714–4722
Fan Zhou and Chengtai Cao. 2021 · 2021
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Multimodal continual graph learning with neural architecture search. In International Conference on World Wide Web . 1292–1300
Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, and Wenwu Zhu. 2022 · 2022
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Memory efficient continual learning for neural text classification
Beyza Ermis, Giovanni Zappella, Martin Wistuba, and Cedric Archambeau. 2022 · 2022
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Graph Lifelong Learning: A Survey
Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, and Charu Aggarwal. 2023 · 2022
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Dygrain: An incremental learning framework for dynamic graphs. In International Joint Conference on Artificial Intelligence . 3157–3163
Seoyoon Kim, Seongjun Yun, and Jaewoo Kang. 2022 · 2022
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Ood-gnn: Out-of-distribution generalized graph neural network
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022 · 2022
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Reinforced continual learning for graphs. In ACM International Conference on Information and Knowledge Management . 1666–1674
Appan Rakaraddi, Lam Siew Kei, Mahardhika Pratama, and Marcus De Carvalho. 2022 · 2022
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Graph few-shot class-incremental learning. In ACM International Conference on Web Search and Data Mining . 987–996
Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu. 2022 · 2022
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Lifelong graph learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition . 13719–13728
Chen Wang, Yuheng Qiu, Dasong Gao, and Sebastian Scherer. 2022 · 2022
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ROLAND: graph learning framework for dynamic graphs. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2358–2366
Jiaxuan You, Tianyu Du, and Jure Leskovec. 2022 · 2022
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Cglb: Benchmark tasks for continual graph learning
Xikun Zhang, Dongjin Song, and Dacheng Tao. 2022a · 2022
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Hierarchical prototype networks for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao. 2022b · 2022
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LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In International Conference on Learning Representations
Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023 · 2023
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Cat: Balanced continual graph learning with graph condensation. In IEEE International Conference on Data Mining . IEEE, 1157–1162
Yilun Liu, Ruihong Qiu, and Zi Huang. 2023 · 2023
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TLC Trip Record Data
NYC Taxi & Limousine Commission. 2023 · 2023
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Self-supervised continual graph learning in adaptive riemannian spaces. In AAAI Conference on Artificial Intelligence , Vol. 37. 4633–4642
Li Sun, Junda Ye, Hao Peng, Feiyang Wang, and S Yu Philip. 2023 · 2023
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Continual learning on dynamic graphs via parameter isolation. In International ACM SIGIR Conference on Research and Development in Information Retrieval . 601–611
Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, and Sunghun Kim. 2023 · 2023
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The official website of the BeGin framework
Jihoon Ko, Shinhwan Kang, Taehyung Kwon, Heechan Moon, and Kijung Shin. 2024 · 2024
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