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Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain.
The reduction of a graph to canonical form and the algebra which appears therein
Weisfeiler, B.; and Leman, A. 1968 · 1968
Earlier work this paper cites.
Arnetminer: extraction and mining of academic social networks
Tang, J.; Zhang, J.; Yao, L.; Li, J.; Zhang, L.; and Su, Z. 2008 · 2008
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2009 · 2009
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J.; and Yang, Q. 2009 · 2009
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Rendle, S.; Freudenthaler, C.; Gantner, Z.; and Schmidt-Thieme, L. 2009 · 2009
Earlier work this paper cites.
A theory of learning from different domains
Ben-David, S.; Blitzer, J.; Crammer, K.; Kulesza, A.; Pereira, F.; and Vaughan, J. W. 2010 · 2010
Earlier work this paper cites.
Weisfeiler-Lehman graph kernels
Shervashidze, N.; Schweitzer, P.; Van Leeuwen, E. J.; Mehlhorn, K.; and Borgwardt, K. M. 2011 · 2011
Earlier work this paper cites.
A kernel two-sample test
Gretton, A.; Borgwardt, K. M.; Rasch, M. J.; Schölkopf, B.; and Smola, A. 2012 · 2012
Earlier work this paper cites.
TrGraph: Cross-network transfer learning via common signature subgraphs
Fang, M.; Yin, J.; Zhu, X.; and Zhang, C. 2015 · 2015
Earlier work this paper cites.
Unsupervised streaming feature selection in social media
Li, J.; Hu, X.; Tang, J.; and Liu, H. 2015 · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
Long, M.; Cao, Y.; Wang, J.; and Jordan, M. 2015 · 2015
Earlier work this paper cites.
Flexible and robust multi-network clustering
Ni, J.; Tong, H.; Fan, W.; and Zhang, X. 2015 · 2015
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R.; and McAuley, J. 2016 · 2016
Earlier work this paper cites.
Deep coral: Correlation alignment for deep domain adaptation
Sun, B.; and Saenko, K. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Hamilton, W.; Ying, Z.; and Leskovec, J. 2017 · 2017
Earlier work this paper cites.
Neural collaborative filtering
He, X.; Liao, L.; Zhang, H.; Nie, L.; Hu, X.; and Chua, T.-S. 2017 · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Cited alongside, same era.
struc2vec: Learning node representations from structural identity
Ribeiro, L. F.; Saverese, P. H.; and Figueiredo, D. R. 2017 · 2017
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CoNet: Collaborative cross networks for cross-domain recommendation
Hu, G.; Zhang, Y.; and Yang, Q. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
Cited alongside, same era.
Representation learning on graphs with jumping knowledge networks
Xu, K.; Li, C.; Tian, Y.; Sonobe, T.; Kawarabayashi, K.-i.; and Jegelka, S. 2018 · 2018
Cited alongside, same era.
On the transferability of spectral graph filters
Levie, R.; Isufi, E.; and Kutyniok, G. 2019 · 2019
Cited alongside, same era.
Adversarial deep network embedding for cross-network node classification
Shen, X.; Dai, Q.; Chung, F.-l.; Lu, W.; and Choi, K.-S. 2020 · 2020
Later among the works it cites.
Investigating and mitigating degree-related biases in graph convoltuional networks
Tang, X.; Yao, H.; Sun, Y.; Wang, Y.; Tang, J.; Aggarwal, C.; Mitra, P.; and Wang, S. 2020 · 2020
Later among the works it cites.
Unsupervised Domain Adaptive Graph Convolutional Networks
Wu, M.; Pan, S.; Zhou, C.; Chang, X.; and Zhu, X. 2020 · 2020
Later among the works it cites.
Learning Personalized Itemset Mapping for Cross-Domain Recommendation
Zhang, Y.; Liu, Y.; Han, P.; Miao, C.; Cui, L.; Li, B.; and Tang, H. 2020 · 2020
Later among the works it cites.
f f -Domain Adversarial Learning: Theory and Algorithms
Acuna, D.; Zhang, G.; Law, M. T.; and Fidler, S. 2021 · 2021
Later among the works it cites.
Representation Subspace Distance for Domain Adaptation Regression
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Simplifying graph convolutional networks
Wu, F.; Souza, A.; Zhang, T.; Fifty, C.; Yu, T.; and Weinberger, K. 2019 · 2019
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Scalable manifold-regularized attributed network embedding via maximum mean discrepancy
Wu, J.; and He, J. 2019 · 2019
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DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Wu, J.; He, J.; and Xu, J. 2019 · 2019
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How Powerful are Graph Neural Networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2019 · 2019
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Cross-domain recommendation via preference propagation graphnet
Zhao, C.; Li, C.; and Fu, C. 2019 · 2019
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On learning invariant representations for domain adaptation
Zhao, H.; Des Combes, R. T.; Zhang, K.; and Gordon, G. 2019 · 2019
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Chen, X.; Wang, S.; Wang, J.; and Long, M. 2021 · 2021
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Let’s agree to degree: Comparing graph convolutional networks in the message-passing framework
Geerts, F.; Mazowiecki, F.; and Perez, G. 2021 · 2021
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Adaptive Transfer Learning on Graph Neural Networks
Han, X.; Huang, Z.; An, B.; and Bai, J. 2021 · 2021
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Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy
Wang, S.; Guan, K.; Wang, Z.; Ainsworth, E. A.; Zheng, T.; Townsend, P. A.; Li, K.; Moller, C.; Wu, G.; and Jiang, C. 2021 · 2021
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Indirect Invisible Poisoning Attacks on Domain Adaptation
Wu, J.; and He, J. 2021 · 2021
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Transfer learning of graph neural networks with ego-graph information maximization
Zhu, Q.; Yang, C.; Xu, Y.; Wang, H.; Zhang, C.; and Han, J. 2021 · 2021
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Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Dai, Q.; Wu, X.-M.; Xiao, J.; Shen, X.; and Wang, D. 2022 · 2022
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Adaptive Knowledge Transfer on Evolving Domains
Wu, J.; Tong, H.; Ainsworth, E.; and He, J. 2022b · 2022
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Domain-adversarial training of neural networks
Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016 · 2030
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Domain Adaptation with Dynamic Open-Set Targets
Wu, J.; and He, J. 2022a · 2049
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