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Non-IID transfer learning on graphs is crucial in many high-stakes domains.
Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F · 2006
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Optimal transport: old and new , volume 338
Villani, C · 2009
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
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Tracking the evolution of communities in dynamic social networks
Greene, D., Doyle, D., and Cunningham, P · 2010
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Eigenspokes: Surprising patterns and scalable community chipping in large graphs
Prakash, B. A., Sridharan, A., Seshadri, M., Machiraju, S., and Faloutsos, C · 2010
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Random walks on temporal networks
Starnini, M., Baronchelli, A., Barrat, A., and Pastor-Satorras, R · 2012
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Graph analysis of functional brain networks: practical issues in translational neuroscience
Fallani, F. D. V., Richiardi, J., Chavez, M., and Achard, S · 2014
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Continuous manifold based adaptation for evolving visual domains
Hoffman, J., Darrell, T., and Saenko, K · 2014
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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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
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Statistical learning theory, 2016
Liang, P · 2016
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A survey on heterogeneous transfer learning
Day, O. and Khoshgoftaar, T. M · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Completely heterogeneous transfer learning with attention-what and what not to transfer
Moon, S. and Carbonell, J. G · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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High quality monocular depth estimation via transfer learning
Alhashim, I. and Wonka, P · 2018
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Transfer learning for time series classification
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M. S., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2018
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Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Yu, B., Yin, H., and Zhu, Z · 2018
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Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., and Salakhutdinov, R · 2019
Cited alongside, same era.
Adagraph: Unifying predictive and continuous domain adaptation through graphs
Mancini, M., Rota Bulò, S., Caputo, B., and Ricci, E · 2019
Cited alongside, same era.
Transfer learning in non-stationary environments
Minku, L. L · 2019
Cited alongside, same era.
Inductive representation learning on temporal graphs
Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K · 2020
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Gcn-se: Attention as explainability for node classification in dynamic graphs
Fan, Y., Yao, Y., and Joe-Wong, C · 2021
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Achieving forgetting prevention and knowledge transfer in continual learning
Ke, Z., Liu, B., Ma, N., Xu, H., and Shu, L · 2021
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Dynamic domain adaptation for efficient inference
Li, S., Zhang, J., Ma, W., Liu, C. H., and Li, W · 2021
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Transfer graph neural networks for pandemic forecasting
Panagopoulos, G., Nikolentzos, G., and Vazirgiannis, M · 2021
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Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
Skarding, J., Gabrys, B., and Musial, K · 2021
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Ortiz-Jiménez, G., Gheche, M. E., Simou, E., Maretic, H. P., and Frossard, P · 2019
Cited alongside, same era.
Transfer learning in natural language processing
Ruder, S., Peters, M. E., Swayamdipta, S., and Wolf, T · 2019
Cited alongside, same era.
Session-based social recommendation via dynamic graph attention networks
Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., and Tang, J · 2019
Cited alongside, same era.
Predictive temporal embedding of dynamic graphs
Taheri, A. and Berger-Wolf, T · 2019
Cited alongside, same era.
Characterizing and avoiding negative transfer
Wang, Z., Dai, Z., Póczos, B., and Carbonell, J · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
An imitation from observation approach to transfer learning with dynamics mismatch
Desai, S., Durugkar, I., Karnan, H., Warnell, G., Hanna, J., and Stone, P · 2020
Cited alongside, same era.
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Afec: Active forgetting of negative transfer in continual learning
Wang, L., Zhang, M., Jia, Z., Li, Q., Bao, C., Ma, K., Zhu, J., and Zhong, Y · 2021
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 2021
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Enhancing cross-lingual transfer by manifold mixup
Yang, H., Chen, H., Zhou, H., and Li, L · 2021
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Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M · 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
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Domain-invariant representation learning from eeg with private encoders
Bethge, D., Hallgarten, P., Grosse-Puppendahl, T., Kari, M., Mikut, R., Schmidt, A., and Özdenizci, O · 2022
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Interpretable graph neural networks for connectome-based brain disorder analysis
Cui, H., Dai, W., Zhu, Y., Li, X., He, L., and Yang, C · 2022
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Molecular contrastive learning with chemical element knowledge graph
Fang, Y., Zhang, Q., Yang, H., Zhuang, X., Deng, S., Zhang, W., Qin, M., Chen, Z., Fan, X., and Chen, H · 2022
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Dygrain: An incremental learning framework for dynamic graphs
Kim, S., Yun, S., and Kang, J · 2022
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Understanding gradual domain adaptation: Improved analysis, optimal path and beyond
Wang, H., Li, B., and Zhao, H · 2022
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A unified meta-learning framework for dynamic transfer learning
Wu, J. and He, J · 2022
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Roland: Graph learning framework for dynamic graphs
You, J., Du, T., and Leskovec, J · 2022
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Segmenting across places: The need for fair transfer learning with satellite imagery
Zhang, M., Singh, H., Chok, L., and Chunara, R · 2022
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Mentorgnn: Deriving curriculum for pre-training gnns
Zhou, D., Zheng, L., Fu, D., Han, J., and He, J · 2022
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Do we really need complicated model architectures for temporal networks?
Cong, W., Zhang, S., Kang, J., Yuan, B., Wu, H., Zhou, X., Tong, H., and Mahdavi, M · 2023
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Non-iid transfer learning on graphs
Wu, J., He, J., and Ainsworth, E. A · 2023
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Towards better dynamic graph learning: New architecture and unified library
Yu, L., Sun, L., Du, B., and Lv, W · 2023
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