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Recently, continual graph learning has been increasingly adopted for diverse graph-structured data processing tasks in non-stationary environments.
Learning to hash with graph neural networks for recommender systems. In Proceedings of The Web Conference 2020 . 1988–1998
Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, and Xia Hu. 2020 · 1998
Earlier work this paper cites.
Lifelong machine learning systems: Beyond learning algorithms. In 2013 AAAI spring symposium series, California, USA, March 25-27, 2013 (AAAI Technical Report) . 49–55
Daniel L Silver, Qiang Yang, and Lianghao Li. 2013 · 2013
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Gated graph sequence neural networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2016 · 2016
Earlier work this paper cites.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell. 2016 · 2016
Earlier work this paper cites.
Expert gate: Lifelong learning with a network of experts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 3366–3375
Rahaf Aljundi, Punarjay Chakravarty, and Tinne Tuytelaars. 2017 · 2017
Earlier work this paper cites.
Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra. 2017 · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry. In International conference on machine learning . PMLR, 1263–1272
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017a · 2017
Earlier work this paper cites.
Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec. 2017b · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Overcoming catastrophic forgetting by incremental moment matching
Sang-Woo Lee, Jin-Hwa Kim, Jaehyun Jun, Jung-Woo Ha, and Byoung-Tak Zhang. 2017 · 2017
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem. 2017 · 2017
Earlier work this paper cites.
Lifelong machine learning: a paradigm for continuous learning
Bing Liu. 2017 · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 6470–6479
David Lopez-Paz and Marc’Aurelio Ranzato. 2017 · 2017
Earlier work this paper cites.
Geometric deep learning on graphs and manifolds using mixture model cnns. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5115–5124
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. 2017 · 2017
Earlier work this paper cites.
Encoder based lifelong learning. In Proceedings of the IEEE International Conference on Computer Vision . 1320–1328
Amal Rannen, Rahaf Aljundi, Matthew B Blaschko, and Tinne Tuytelaars. 2017 · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 5533–5542
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. 2017 · 2017
Earlier work this paper cites.
Continual learning with deep generative replay. In Proceedings of the 31st International Conference on Neural Information Processing Systems , Vol. 30. 2994–3003
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. 2017 · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, Toulon, France, April 24-26, 2017
Max Welling and Thomas N Kipf. 2017 · 2017
Earlier work this paper cites.
Continual learning through synaptic intelligence. In International Conference on Machine Learning . PMLR, 3987–3995
Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017 · 2017
Earlier work this paper cites.
Memory aware synapses: Learning what (not) to forget. In Proceedings of the European Conference on Computer Vision . 139–154
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars. 2018 · 2018
Earlier work this paper cites.
A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018 · 2018
Earlier work this paper cites.
End-to-end incremental learning. In Proceedings of the European conference on computer vision (ECCV) . 233–248
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari. 2018 · 2018
Earlier work this paper cites.
Riemannian walk for incremental learning: Understanding forgetting and intransigence. In Proceedings of the European Conference on Computer Vision (ECCV) . 532–547
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr. 2018 · 2018
Earlier work this paper cites.
A survey on network embedding
Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018 · 2018
Earlier work this paper cites.
MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf. 2018 · 2018
Earlier work this paper cites.
Don’t forget, there is more than forgetting: new metrics for Continual Learning. In Workshop on Continual Learning, NeurIPS 2018
Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, and Davide Maltoni. 2018 · 2018
Earlier work this paper cites.
Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara. 2018 · 2018
Earlier work this paper cites.
Re-evaluating continual learning scenarios: A categorization and case for strong baselines. In Continual Learning Workshop, 32nd Conference on Neural Information Processing Systems (NIPS 2018)
Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, and Zsolt Kira. 2018 · 2018
Earlier work this paper cites.
Selective experience replay for lifelong learning. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2018) (New Orleans, Louisiana, USA). Article 404, 8 pages
David Isele and Akansel Cosgun. 2018 · 2018
Earlier work this paper cites.
Less-forgetful learning for domain expansion in deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim. 2018 · 2018
Earlier work this paper cites.
Continual classification learning using generative model. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS) 2018 . 3-8 December 2018
Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, and Alexandros Kalousis. 2018 · 2018
Earlier work this paper cites.
Rotate your networks: Better weight consolidation and less catastrophic forgetting. In 2018 24th International Conference on Pattern Recognition (ICPR) . IEEE, 2262–2268
Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov. 2018 · 2018
Earlier work this paper cites.
Packnet: Adding multiple tasks to a single network by iterative pruning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition . 7765–7773
Arun Mallya and Svetlana Lazebnik. 2018 · 2018
Earlier work this paper cites.
Never-ending learning
Tom Mitchell, William Cohen, Estevam Hruschka, Partha Talukdar, Bishan Yang, Justin Betteridge, Andrew Carlson, Bhavana Dalvi, Matt Gardner, Bryan Kisiel, et al · 2018
Earlier work this paper cites.
Overcoming catastrophic forgetting with hard attention to the task. In International Conference on Machine Learning . PMLR, 4548–4557
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou. 2018 · 2018
Earlier work this paper cites.
Graphvae: Towards generation of small graphs using variational autoencoders. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27 . Springer, 412–422
Martin Simonovsky and Nikos Komodakis. 2018 · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Reinforced continual learning. In Proceedings of the 32nd International Conference on Neural Information Processing Systems . 907–916
Ju Xu and Zhanxing Zhu. 2018 · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence . 3634–3640
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018 · 2018
Earlier work this paper cites.
From one-off machine learning to perpetual learning: A step perspective. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, 17–23
Du Zhang. 2018 · 2018
Earlier work this paper cites.
Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining . 2847–2856
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Task-free continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11254–11263
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars. 2019 · 2019
Earlier work this paper cites.
Continual learning with neural networks: A review. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data . 362–365
Abhijeet Awasthi and Sunita Sarawagi. 2019 · 2019
Earlier work this paper cites.
Multi-label image recognition with graph convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 5177–5186
Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Yanwen Guo. 2019 · 2019
Earlier work this paper cites.
Graph neural networks for social recommendation. In The world wide web conference, San Francisco, CA, USA, May 13-17, 2019 . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Earlier work this paper cites.
Hypergraph neural networks. In Proceedings of the 33th AAAI conference on artificial intelligence, Honolulu, Hawaii, USA, January 27 - February 1, 2019 , Vol. 33. 3558–3565
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. 2019 · 2019
Earlier work this paper cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
Jonathan Frankle and Michael Carbin. 2019 · 2019
Earlier work this paper cites.
Learning a unified classifier incrementally via rebalancing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 831–839
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. 2019 · 2019
Earlier work this paper cites.
Fast interactive object annotation with curve-gcn. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 5257–5266
Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, and Sanja Fidler. 2019 · 2019
Earlier work this paper cites.
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019 · 2019
Earlier work this paper cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. 2019 · 2019
Cited alongside, same era.
Three scenarios for continual learning
Gido M Van de Ven and Andreas S Tolias. 2019 · 2019
Cited alongside, same era.
How powerful are graph neural networks?. In 7th International Conference on Learning Representations, New Orleans, LA, USA, May 6-9, 2019
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Cited alongside, same era.
A survey on graph neural networks for knowledge graph completion
Siddhant Arora. 2020 · 2020
Cited alongside, same era.
A gentle introduction to deep learning for graphs
Davide Bacciu, Federico Errica, Alessio Micheli, and Marco Podda. 2020 · 2020
Cited alongside, same era.
Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021 · 2021
Later among the works it cites.
Contrastive self-supervised learning for graph classification. In Proceedings of the AAAI conference on Artificial Intelligence , Vol. 35. 10824–10832
Jiaqi Zeng and Pengtao Xie. 2021 · 2021
Later among the works it cites.
Task-agnostic continual learning using online variational bayes with fixed-point updates
Chen Zeno, Itay Golan, Elad Hoffer, and Daniel Soudry. 2021 · 2021
Later among the works it cites.
Graph neural networks and their current applications in bioinformatics
Xiao-Meng Zhang, Li Liang, Lin Liu, and Ming-Jing Tang. 2021b · 2021
Later among the works it cites.
Overcoming catastrophic forgetting in graph neural networks with experience replay. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 4714–4722
Fan Zhou and Chengtai Cao. 2021 · 2021
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Magdalena Biesialska, Katarzyna Biesialska, and Marta R. Costa-jussà. 2020 · 2020
Cited alongside, same era.
Continual learning in low-rank orthogonal subspaces
Arslan Chaudhry, Naeemullah Khan, Puneet Dokania, and Philip Torr. 2020 · 2020
Cited alongside, same era.
Scalable deep generative modeling for sparse graphs. In International conference on machine learning . PMLR, 2302–2312
Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai, and Dale Schuurmans. 2020 · 2020
Cited alongside, same era.
Podnet: Pooled outputs distillation for small-tasks incremental learning. In ECCV 2020-16th European Conference on Computer Vision, Glasgow, UK, August 23–28, 2020 , Vol. 12365. Springer, 86–102
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle. 2020 · 2020
Cited alongside, same era.
Orthogonal gradient descent for continual learning. In International Conference on Artificial Intelligence and Statistics . PMLR, 3762–3773
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Cited alongside, same era.
Embracing change: Continual learning in deep neural networks
Raia Hadsell, Dushyant Rao, Andrei A Rusu, and Razvan Pascanu. 2020 · 2020
Cited alongside, same era.
Graph representation learning
William L Hamilton. 2020 · 2020
Cited alongside, same era.
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Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, and Wenwu Zhu. 2022 · 2022
Later among the works it cites.
Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark
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Machine Learning on Graphs: A Model and Comprehensive Taxonomy
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Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, and Yizhou Yu. 2022 · 2022
Later among the works it cites.
Causal incremental graph convolution for recommender system retraining
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Later among the works it cites.
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Chen Gao, Xiang Wang, Xiangnan He, and Yong Li. 2022 · 2022
Later among the works it cites.
Adaptive orthogonal projection for batch and online continual learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 6783–6791
Yiduo Guo, Wenpeng Hu, Dongyan Zhao, and Bing Liu. 2022 · 2022
Later among the works it cites.
Vision gnn: An image is worth graph of nodes
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Later among the works it cites.
Graph neural network for traffic forecasting: A survey
Weiwei Jiang and Jiayun Luo. 2022 · 2022
Later among the works it cites.
Heterogeneous feature augmentation for ponzi detection in ethereum
Chengxiang Jin, Jie Jin, Jiajun Zhou, Jiajing Wu, and Qi Xuan. 2022 · 2022
Later among the works it cites.
DyGRAIN: An Incremental Learning Framework for Dynamic Graphs. In 31st International Joint Conference on Artificial Intelligence, IJCAI 2022 . International Joint Conferences on Artificial Intelligence, 3157–3163
Seoyoon Kim, Seongjun Yun, and Jaewoo Kang. 2022 · 2022
Later among the works it cites.
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Jihoon Ko, Shinhwan Kang, and Kijung Shin. 2022 · 2022
Later among the works it cites.
Finding global homophily in graph neural networks when meeting heterophily. In International Conference on Machine Learning . PMLR, 13242–13256
Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. 2022 · 2022
Later among the works it cites.
Graph self-supervised learning: A survey
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip Yu. 2022 · 2022
Later among the works it cites.
Geometer: Graph few-shot class-incremental learning via prototype representation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1152–1161
Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang, Luoyi Fu, and Xinbing Wang. 2022 · 2022
Later among the works it cites.
Online continual learning in image classification: An empirical survey
Zheda Mai, Ruiwen Li, Jihwan Jeong, David Quispe, Hyunwoo Kim, and Scott Sanner. 2022 · 2022
Later among the works it cites.
Interpretable and generalizable graph learning via stochastic attention mechanism. In International Conference on Machine Learning . PMLR, 15524–15543
Siqi Miao, Mia Liu, and Pan Li. 2022 · 2022
Later among the works it cites.
Learning on streaming graphs with experience replay. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing . 470–478
Massimo Perini, Giorgia Ramponi, Paris Carbone, and Vasiliki Kalavri. 2022 · 2022
Later among the works it cites.
A Theory for Knowledge Transfer in Continual Learning. In Conference on Lifelong Learning Agents . PMLR, 647–660
Diana Benavides Prado and Patricia Riddle. 2022 · 2022
Later among the works it cites.
Reinforced continual learning for graphs. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 1666–1674
Appan Rakaraddi, Lam Siew Kei, Mahardhika Pratama, and Marcus De Carvalho. 2022 · 2022
Later among the works it cites.
Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2022 · 2022
Later among the works it cites.
Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks
Khadija Shaheen, Muhammad Abdullah Hanif, Osman Hasan, and Muhammad Shafique. 2022 · 2022
Later among the works it cites.
A survey of graph neural networks for social recommender systems
Kartik Sharma, Yeon-Chang Lee, Sivagami Nambi, Aditya Salian, Shlok Shah, Sang-Wook Kim, and Srijan Kumar. 2022 · 2022
Later among the works it cites.
Graph few-shot class-incremental learning. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . 987–996
Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu. 2022 · 2022
Later among the works it cites.
A review on graph neural network methods in financial applications
Jianian Wang, Sheng Zhang, Yanghua Xiao, and Rui Song. 2022b · 2022
Later among the works it cites.
Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2022 · 2022
Later among the works it cites.
Dynamic network embedding survey
Guotong Xue, Ming Zhong, Jianxin Li, Jia Chen, Chengshuai Zhai, and Ruochen Kong. 2022 · 2022
Later among the works it cites.
Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022 · 2022
Later among the works it cites.
Cglb: Benchmark tasks for continual graph learning
Xikun Zhang, Dongjin Song, and Dacheng Tao. 2022a · 2022
Later among the works it cites.
Hierarchical prototype networks for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao. 2022b · 2022
Later among the works it cites.
Graph neural networks for graphs with heterophily: A survey
Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu. 2022a · 2022
Later among the works it cites.
Rethinking and scaling up graph contrastive learning: An extremely efficient approach with group discrimination
Yizhen Zheng, Shirui Pan, Vincent Lee, Yu Zheng, and Philip S Yu. 2022b · 2022
Later among the works it cites.
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey
Yuecai Zhu, Fuyuan Lyu, Chengming Hu, Xi Chen, and Xue Liu. 2022 · 2022
Later among the works it cites.
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Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, and Charu Aggarwal. 2023 · 2023
Later among the works it cites.
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Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, et al · 2023
Later among the works it cites.
Jiayan Guo, Lun Du, and Hengyu Liu. 2023 · 2023
Later among the works it cites.
Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin
Wai Weng Lo, Gayan K Kulatilleke, Mohanad Sarhan, Siamak Layeghy, and Marius Portmann. 2023 · 2023
Later among the works it cites.
Graph Neural Networks for Intelligent Transportation Systems: A Survey
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, and Zachary Patterson. 2023 · 2023
Later among the works it cites.
ICICLE: Interpretable Class Incremental Continual Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 1887–1898
Dawid Rymarczyk, Joost van de Weijer, Bartosz Zieliński, and Bartlomiej Twardowski. 2023 · 2023
Later among the works it cites.
Towards robust graph incremental learning on evolving graphs. In International Conference on Machine Learning . PMLR, 32728–32748
Junwei Su, Difan Zou, Zijun Zhang, and Chuan Wu. 2023 · 2023
Later among the works it cites.
Self-supervised continual graph learning in adaptive riemannian spaces. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 4633–4642
Li Sun, Junda Ye, Hao Peng, Feiyang Wang, and S Yu Philip. 2023 · 2023
Later among the works it cites.
Can Language Models Solve Graph Problems in Natural Language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2023a · 2023
Later among the works it cites.
A Comprehensive Survey of Continual Learning: Theory, Method and Application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu. 2023b · 2023
Later among the works it cites.
Graph neural networks for natural language processing: A survey
Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long, et al · 2023
Later among the works it cites.
Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive
Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z Li. 2023b · 2023
Later among the works it cites.
Continual Graph Learning: A Survey
Qiao Yuan, Sheng-Uei Guan, Pin Ni, Tianlun Luo, Ka Lok Man, Prudence Wong, and Victor Chang. 2023 · 2023
Later among the works it cites.
Large graph models: A perspective
Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu. 2023 · 2023
Later among the works it cites.