IEEE Computational Intelligence Magazine - February 2023 - 51

[66] Y. Ma, Z. Guo, Z. Ren, J. Tang, and D. Yin, " Streaming graph neural networks, "
in Proc. 43rd Int. ACMSIGIRConf. Res. Develop. Inf. Retrieval, 2020, pp. 719-728.
[67] S. Sun, Q. Sun, K. Zhou, and T. Lv, " Hierarchical attention prototypical networks
for few-shot text classification, " in Proc. Conf. Empirical Methods Natural Lang.
Process. 9th Int.Joint Conf. Natural Lang. Process., 2019, pp. 476-485.
[68] X. Kou, Y. Lin, S. Liu, P. Li, J. Zhou, and Y. Zhang, " Disentangle-based continual
graph representation learning, " in Proc. Conf. Empirical Methods Natural Lang.
Process. (EMNLP), 2020.
[69] Z. Tan, K. Ding, R. Guo, and H. Liu, " Graph few-shot class-incremental learning, "
in Proc. 15th ACMInt. Conf. Web Search Data Mining, 2022, pp. 987-996.
[70] H. Liu, Y. Yang, and X. Wang, " Overcoming catastrophic forgetting in
graph neural networks, " in Proc. AAAI Conf. Artif. Intell., 2021, vol. 35,
pp. 8653-8661.
[71] H.-J. Song and S.-B. Park, " Enriching translation-based knowledge graph
embeddings through continual learning, " IEEE Access, vol. 6, pp. 60489-60497,
2018.
[72] H. Wei, G. Hu, W. Bai, S. Xia, and Z. Pan, " Lifelong representation learning in
dynamic attributed networks, " Neurocomputing, vol. 358, pp. 1-9, Sep. 2019.
[73] J. Chen, T. Ma, and C. Xiao, " FastGCN: Fast learning with graph convolutional
networks via importance sampling, " in Proc. Int. Conf. Learn. Representations
(ICLR), 2018.
[74] A. Krizhevsky, I. Sutskever, and G. E. Hinton, " Imagenet classification
with deep convolutional neural networks, " Commun. ACM, vol. 60, no. 6,
pp. 84-90, 2017.
[75] P.Sen, G.Namata,M. Bilgic,L.Getoor,B.Galligher,and T.Eliassi-Rad,
" Collective classification in network data, " AI Mag., vol. 29, no. 3, pp. 93-93,
2008.
[76] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, " Distributed
representations of words and phrases and their compositionality, " Proc. Adv. Neural
Inf. Process. Syst., vol. 26, pp. 3111-3119, 2013.
[77] K. Wang, Z. Shen, C. Huang, C.-H. Wu, Y. Dong, and A. Kanakia, " Microsoft
academic graph: When experts are not enough, " Quantitative Sci. Stud.,vol. 1,
no. 1, pp. 396-413, 2020.
[78] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio,
" Graph attention networks, " in Proc. Int. Conf. Learn. Representations (ICLR),
2018.
[79] J. Klicpera, A. Bojchevski, and S. G€unnemann, " Predict then propagate: Graph
neural networks meet personalized pagerank, " in Proc. Int. Conf. Learn. Representations
(ICLR), 2018.
[80] K. Xu, W. Hu, J. Leskovec, and S. Jegelka, " How powerful are graph neural
networks ?, " in Proc. Int. Conf. Learn. Representations (ICLR), 2018.
[81] H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang, " Geom-GCN: Geometric
graph convolutional networks, " in Proc. Int. Conf. Learn. Representations (ICLR),
2020.
[82] T. de Bruin, J. Kober, K. Tuyls, and R. Babuska, " Improved deep reinforcement
learning for robotics through distribution-based experience retention, " in Proc.
IEEE/RSJ Int. Conf. Intell. Robots Syst., 2016, pp. 3947-3952.
[83] D. Kumaran, D. Hassabis, and J. L. McClelland, " What learning systems do
intelligent agents need? Complementary learning systems theory updated, " Trends
Cogn. Sci., vol. 20, no. 7, pp. 512-534, 2016.
[84] D. P. Kingma and J. Ba, " Adam: A method for stochastic optimization, " in Proc.
Int. Conf. Learn. Representations (ICLR), 2015.
[85] B. Perozzi, R. Al-Rfou, and S. Skiena, " Deepwalk: Online learning of social
representations, " in Proc. 20th ACMSIGKDD Int. Conf. Knowl. Discov. Data Mining,
2014, pp. 701-710.
[86] A. Grover and J. Leskovec, " node2vec: Scalable feature learning for networks, "
in Proc.22ndACM SIGKDD Int. Conf.Knowl.Discov. Data Mining,
2016, pp. 855-864.
[87] V. P. Dwivedi, C. K. Joshi, T. Laurent, Y. Bengio, and X. Bresson, " Benchmarking
graph neural networks, " 2020, arXive:2003.00982.
[88] J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su, " Arnetminer: Extraction
and mining of academic social networks, " in Proc. 14th ACM SIGKDD Int. Conf.
Knowl. Discov. Data Mining, 2008, pp. 990-998.
[89] Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, " Rotate: Knowledge graph
embedding by relational rotation in complex space, " in Proc. Int. Conf. Learn. Representations
(ICLR), 2019.
[90] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko,
" Translating embeddings for modeling multi-relational data, " in Proc. 26th Int. Conf.
Neural Inf. Process. Syst., 2013, pp. 2787-2795.
[91] M. Henaff, J. Bruna, and Y. LeCun, " Deep convolutional networks on graphstructured
data, " 2015, arXiv:1506.05163.
[92] K. Toutanova and D. Chen, " Observed versus latent features for knowledge
base and text inference, " in Proc. 3rd Workshop Continuous Vector Space Models Their
Compositionality, 2015, pp. 57-66.
[93] T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, " Convolutional 2D
knowledge graph embeddings, " in Proc. AAAI Conf. Artif. Intell., vol. 32, 2018,
pp. 1811-1818.
[94] T. D. Nguyen, D. Q. Nguyen, and D. Q. Phung, " A novel embedding model
for knowledge base completion based on convolutional neural network, " in Proc.
Conf. North Amer. Chap. Assoc. Comput. Linguistics: Human Lang. Technol., 2018,
pp. 327-333.
[95] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, " Focal loss for
dense object detection, " in Proc. IEEE Int. Conf. Comput. Vis., 2017,
pp. 2980-2988.
[96] A. K. Menon, S. Jayasumana, A. S. Rawat, H. Jain, A. Veit, and S. Kumar,
" Long-tail learning via logit adjustment, " in Proc. Int. Conf. Learn. Representations
(ICLR), 2020.
[97] J. McAuley, R. Pandey, and J. Leskovec, " Inferring networks of substitutable
and complementary products, " in Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discov.
Data Mining, 2015, pp. 785-794.
[98] C. Zhang, N. Song, G. Lin, Y. Zheng, P. Pan, and Y. Xu, " Few-shot incremental
learning with continually evolved classifiers, " in Proc. IEEE/CVF Conf. Comput.
Vis. Pattern Recognit., 2021, pp. 12455-12464.
[99] F. Zenke, B. Poole, and S. Ganguli, " Continual learning through synaptic intelligence, "
in Proc. Int. Conf. Mach. Learn., 2017, pp. 3987-3995.
[100] A. Bojchevski and S. G€unnemann, " Deep Gaussian embedding of graphs:
Unsupervised inductive learning via ranking, " in Proc. Int. Conf. Learn. Representations
(ICLR), 2018.
[101] G. I. Parisi, J. Tani, C. Weber, and S. Wermter, " Lifelong learning of spatiotemporal
representations with dual-memory recurrent self-organization, " Front. Neurorobot.,
vol. 12, 2018, Art. no. 78.
[102] J. S. Vitter, " Random sampling with a reservoir, " ACM Trans. Math. Softw.,
vol. 11, no. 1, pp. 37-57, 1985.
[103] M. Weber et al., " Anti-money laundering in bitcoin: Experimenting with
graph convolutional networks for financial forensics, " 2019, arXiv:1908.02591.
[104] O. Benyahia, C. Largeron, B. Jeudy, and O. R. Zaïane, " Dancer: Dynamic
attributed network with community structure generator, " in Proc. Joint Eur. Conf.
Mach. Learn. Knowl. Discov. Databases, 2016, pp. 41-44.
[105] D. Wang, P. Cui, and W. Zhu, " Structural deep network embedding, " in
Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2016,
pp. 1225-1234.
[106] Z. Chen, N. Ma, and B. Liu, " Lifelong learning for sentiment classification, " in
Proc. 53rd Annu. Meeting Assoc. Comput. Linguistics, 7th Int. Joint Conf. Natural Lang.
Process., 2015, pp. 750-756.
[107] C. Chen, K. Petty, A. Skabardonis, P. Varaiya, and Z. Jia, " Freeway performance
measurement system: Mining loop detector data, " Transp. Res. Rec.,
vol. 1748, no. 1, pp. 96-102, 2001.
[108] C. Song, Y. Lin, S. Guo, and H. Wan, " Spatial-temporal synchronous graph
convolutional networks: A new framework for spatial-temporal network data forecasting, "
in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, pp. 914-921.
[109] K. Cho et al., " Learning phrase representations using RNN encoder-decoder
for statistical machine translation, " in Proc. Conf. Emp. Methods Natural Lang. Process.
(EMNLP), 2014.
[110] R. Qu, G. Kendall, and N. Pillay, " The general combinatorial optimization
problem: Towards automated algorithm design, " IEEE Comput. Intell. Mag., vol. 15,
no. 2, pp. 14-23, May 2020.
[111] W.-L. Chiang, X. Liu, S. Si, Y. Li, S. Bengio, and C.-J. Hsieh, " ClusterGCN:
An efficient algorithm for training deep and large graph convolutional
networks, " in Proc.25thACM SIGKDD Int. Conf.Knowl.Discov. Data Mining,
2019, pp. 257-266.
[112] D. Szklarczyk et al., " String v11: Protein-protein association networks with
increased coverage, supporting functional discovery in genome-wide experimental
datasets, " Nucleic Acids Res., vol. 47, no. D1, pp. D607-D613, 2019.
[113] A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny, " Efficient
lifelong learning with a-gem, " in Proc. Int. Conf. Learn. Representations (ICLR),
2018.
[114] M. McCloskey and N. J. Cohen, " Catastrophic interference in connectionist
networks: The sequential learning problem, " in Psychology ofLearning and Motivation,
vol. 24. Amsterdam, The Netherlands: Elsevier, 1989, pp. 109-165.
[115] E. Min et al., " Transformer for graphs: An overview from architecture perspective, "
2022, arXiv:2202.08455.
[116] C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, " A survey on federated
learning, " Knowl.-Based Syst., vol. 216, 2021, Art. no. 106775.
[117] H. Zhang, T. Shen, F. Wu, M. Yin, H. Yang, and C. Wu, " Federated graph
learning-A position paper, " 2021, arXiv:2105.11099.
[118] A. Usmanova, F. Portet, P. Lalanda, and G. Vega, " A distillation-based
approach integrating continual learning and federated learning for pervasive services, "
2021, arXiv:2109.04197.
FEBRUARY 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 51
IEEE Computational Intelligence Magazine - February 2023

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - February 2023
