Fetching the paper…
Reading the bibliography…
Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes.
Spectral graph theory
Fan RK Chung · 1997
Earlier work this paper cites.
Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
Earlier work this paper cites.
A tutorial on spectral clustering
Ulrike Von Luxburg · 2007
Earlier work this paper cites.
An overview of microsoft academic service (mas) and applications
Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Hsu, and Kuansan Wang · 2015
Earlier work this paper cites.
Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 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.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Earlier work this paper cites.
Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Earlier work this paper cites.
Continual learning with hypernetworks
Johannes Von Oswald, Christian Henning, Benjamin F Grewe, and João Sacramento · 2019
Earlier work this paper cites.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Earlier work this paper cites.
Conditional channel gated networks for task-aware continual learning
Davide Abati, Jakub Tomczak, Tijmen Blankevoort, Simone Calderara, Rita Cucchiara, and Babak Ehteshami Bejnordi · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
Earlier work this paper cites.
Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2020
Earlier work this paper cites.
Posterior meta-replay for continual learning
Christian Henning, Maria Cervera, Francesco D’Angelo, Johannes Von Oswald, Regina Traber, Benjamin Ehret, Seijin Kobayashi, Benjamin F Grewe, and Joao Sacramento · 2021
Cited alongside, same era.
Overcoming catastrophic forgetting in graph neural networks
Huihui Liu, Yiding Yang, and Xinchao Wang · 2021
Cited alongside, same era.
Overcoming catastrophic forgetting in graph neural networks with experience replay
Fan Zhou and Chengtai Cao · 2021
Cited alongside, same era.
A theoretical study on solving continual learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, Zixuan Ke, and Bing Liu · 2022
Cited alongside, same era.
Learning on streaming graphs with experience replay
Massimo Perini, Giorgia Ramponi, Paris Carbone, and Vasiliki Kalavri · 2022
Cited alongside, same era.
Reinforced continual learning for graphs
Appan Rakaraddi, Lam Siew Kei, Mahardhika Pratama, and Marcus De Carvalho · 2022
Learnability and algorithm for continual learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi, and Bing Liu · 2023
Later among the works it cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
Later among the works it cites.
Cat: Balanced continual graph learning with graph condensation
Yilun Liu, Ruihong Qiu, and Zi Huang · 2023
Later among the works it cites.
Towards robust graph incremental learning on evolving graphs
Junwei Su, Difan Zou, Zijun Zhang, and Chuan Wu · 2023
Later among the works it cites.
Self-supervised continual graph learning in adaptive riemannian spaces
Li Sun, Junda Ye, Hao Peng, Feiyang Wang, and S Yu Philip · 2023
Later among the works it cites.
All in one: Multi-task prompting for graph neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Lifelong graph learning
Chen Wang, Yuheng Qiu, Dasong Gao, and Sebastian Scherer · 2022
Cited alongside, same era.
Dualprompt: Complementary prompting for rehearsal-free continual learning
Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, et al · 2022
Cited alongside, same era.
Learning to prompt for continual learning
Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister · 2022
Cited alongside, same era.
Cglb: Benchmark tasks for continual graph learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2022
Cited alongside, same era.
Hierarchical prototype networks for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2022
Cited alongside, same era.
Sparsified subgraph memory for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2022
Cited alongside, same era.
Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan · 2023
Later among the works it cites.
Graph prompt learning: A comprehensive survey and beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li · 2023
Later among the works it cites.
Continual learning on dynamic graphs via parameter isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, and Sunghun Kim · 2023
Later among the works it cites.
Continual learning on dynamic graphs via parameter isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, and Sunghun Kim · 2023
Later among the works it cites.
Ricci curvature-based graph sparsification for continual graph representation learning
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2023
Later among the works it cites.
Class incremental learning via likelihood ratio based task prediction
Haowei Lin, Yijia Shao, Weinan Qian, Ningxin Pan, Yiduo Guo, and Bing Liu · 2024
Closest in time.
Graph continual learning with debiased lossless memory replay
Chaoxi Niu, Guansong Pang, and Ling Chen · 2024
Closest in time.
Continual learning on graphs: A survey
Zonggui Tian, Du Zhang, and Hong-Ning Dai · 2024
Closest in time.
A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2024
Closest in time.
Continual learning on graphs: Challenges, solutions, and opportunities
Xikun Zhang, Dongjin Song, and Dacheng Tao · 2024
Closest in time.