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Continual graph learning (CGL) studies the problem of learning from an infinite stream of graph data, consolidating historical knowledge, and generalizing it to the future task.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Arnetminer: Extraction and mining of academic social networks
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su · 2008
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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Robust mapping learning for multi-view multi-label classification with missing labels
Weijieying Ren, Lei Zhang, Bo Jiang, Zhefeng Wang, Guangming Guo, and Guiquan Liu · 2017
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Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 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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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Simple embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi and David Poole · 2018
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Tracking and forecasting dynamics in crowdfunding: A basis-synthesis approach
Xiaoying Ren, Linli Xu, Tianxiang Zhao, Chen Zhu, Junliang Guo, and Enhong Chen · 2018
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Zero-shot learning: An energy based approach
Tianxiang Zhao, Guiquan Liu, Chao Ma, Enhong Chen, et al · 2018
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
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Enhancing semantic representations of bilingual word embeddings with syntactic dependencies
Linli Xu, Wenjun Ouyang, Xiaoying Ren, Yang Wang, and Liang Jiang · 2018
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The lottery ticket hypothesis: Training pruned neural networks
Jonathan Frankle and Michael Carbin · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
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Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
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On asymptotic behaviors of graph cnns from dynamical systems perspective
Kenta Oono and Taiji Suzuki · 2019
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Reliable graph neural networks via robust aggregation
Simon Geisler, Daniel Zügner, and Stephan Günnemann · 2020
Cited alongside, same era.
Semi-supervised graph-to-graph translation
Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, and Suhang Wang · 2020
Cited alongside, same era.
Balancing quality and human involvement: An effective approach to interactive neural machine translation
Tianxiang Zhao, Lemao Liu, Guoping Huang, Huayang Li, Yingling Liu, Liu GuiQuan, and Shuming Shi · 2020
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 · 2020
Cited alongside, same era.
Overcoming catastrophic forgetting in graph neural networks
Huihui Liu, Yiding Yang, and Xinchao Wang · 2020
Cited alongside, same era.
Explanation guided contrastive learning for sequential recommendation
Lei Wang, Ee-Peng Lim, Zhiwei Liu, and Tianxiang Zhao · 2022
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GOOD: A graph out-of-distribution benchmark
Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji · 2022
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Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective
Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Lim Ee Peng, and Yanjie Fu · 2022
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Learning invariant graph representations for out-of-distribution generalization
Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu · 2022
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Ego-graph replay based continual learning for misinformation engagement prediction
Hongbo Bo, Ryan McConville, Jun Hong, and Weiru Liu · 2022
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Multimodal continual graph learning with neural architecture search
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Han Qiu, Qinkai Zheng, Mounira Msahli, Gerard Memmi, Meikang Qiu, and Jialiang Lu · 2021
Cited alongside, same era.
Graph neural networks for automated de novo drug design
Jiacheng Xiong, Zhaoping Xiong, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng · 2021
Cited alongside, same era.
Fair and effective policing for neighborhood safety: understanding and overcoming selection biases
Weijeiying Ren, Kunpeng Liu, Tianxiang Zhao, and Yanjie Fu · 2021
Cited alongside, same era.
Graphsmote: Imbalanced node classification on graphs with graph neural networks
Tianxiang Zhao, Xiang Zhang, and Suhang Wang · 2021
Cited alongside, same era.
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2021
Cited alongside, same era.
Effective sparsification of neural networks with global sparsity constraint
Xiao Zhou, Weizhong Zhang, Hang Xu, and Tong Zhang · 2021
Cited alongside, same era.
Trafficstream: A streaming traffic flow forecasting framework based on graph neural networks and continual learning
Xu Chen, Junshan Wang, and Kunqing Xie · 2021
Cited alongside, same era.
Jie Cai, Xin Wang, Chaoyu Guan, Yateng Tang, Jin Xu, Bin Zhong, and Wenwu Zhu · 2022
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Reinforced continual learning for graphs
Appan Rakaraddi, Lam Siew Kei, Mahardhika Pratama, and Marcus de Carvalho · 2022
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On consistency in graph neural network interpretation
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, and Suhang Wang · 2022
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Topoimb: Toward topology-level imbalance in learning from graphs
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, and Suhang Wang · 2022
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Synthetic over-sampling for imbalanced node classification with graph neural networks
Tianxiang Zhao, Xiang Zhang, and Suhang Wang · 2022
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Semi-supervised drifted stream learning with short lookback
Weijieying Ren, Pengyang Wang, Xiaolin Li, Charles E Hughes, and Yanjie Fu · 2022
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Adaptive trajectory prediction via transferable gnn
Yi Xu, Lichen Wang, Yizhou Wang, and Yun Fu · 2022
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Non-iid transfer learning on graphs
Jun Wu, Jingrui He, and Elizabeth Ainsworth · 2022
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Fedni: Federated graph learning with network inpainting for population-based disease prediction
Liang Peng, Nan Wang, Nicha Dvornek, Xiaofeng Zhu, and Xiaoxiao Li · 2022
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Forget-free continual learning with winning subnetworks
Haeyong Kang, Rusty John Lloyd Mina, Sultan Rizky Hikmawan Madjid, Jaehong Yoon, Mark Hasegawa-Johnson, Sung Ju Hwang, and Chang D. Yoo · 2022
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Faithful and consistent graph neural network explanations with rationale alignment
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, and Suhang Wang · 2023
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Skill disentanglement for imitation learning from suboptimal demonstrations
Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, and Haifeng Chen · 2023
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Towards faithful and consistent explanations for graph neural networks
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, and Suhang Wang · 2023
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