Enhancing Graph Neural Networks via auxiliary training for semi-supervised node classification

Knowledge-Based Systems - Tập 220 - Trang 106884 - 2021
Yao Wu1, Yu Song1, Hong Huang1, Fanghua Ye2, Xing Xie3, Hai Jin1
1National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, China
2University College London, London, United Kingdom
3Microsoft Research Asia, Beijing, China

Tài liệu tham khảo

Van Noorden, 2014, Online collaboration: Scientists and the social network, Nature News, 512, 126, 10.1038/512126a Tang, 2008, Arnetminer: extraction and mining of academic social networks, 990 Wu, 2021, A comprehensive survey on graph neural networks, IEEE Trans. Neural Netw. Learn. Syst., 32, 4, 10.1109/TNNLS.2020.2978386 Parisot, 2018, Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease, Med. Image Anal., 48, 117, 10.1016/j.media.2018.06.001 Zha, 2009, Graph-based semi-supervised learning with multiple labels, J. Vis. Commun. Image Represent., 20, 97, 10.1016/j.jvcir.2008.11.009 Wu, 2014, Semi-supervised multi-label collective classification ensemble for functional genomics, BMC Genomics, 15, S17, 10.1186/1471-2164-15-S9-S17 Xiao-Ming Wu, Zhenguo Li, Anthony M. So, John Wright, Shih-Fu Chang, Learning with partially absorbing random walks, in: Proceedings of NeurIPS, 2012, pp. 3077–3085. Caruana, 1997, Multitask learning, Mach. Learn., 28, 41, 10.1023/A:1007379606734 Will Hamilton, Zhitao Ying, Jure Leskovec, Inductive representation learning on large graphs, in: Proceedings of NeurIPS, 2017, pp. 1024–1034. Wen-bing Huang, Tong Zhang, Yu Rong, Junzhou Huang, Adaptive sampling towards fast graph representation learning, in: Proceedings of NeurIPS, 2018, pp. 4563–4572. Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger, Simplifying graph convolutional networks, in: Proceedings of ICML, 2019, pp. 6861–6871. Thomas N. Kipf, Max Welling, Semi-supervised classification with graph convolutional networks, in: Proceedings of ICLR, 2017. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio, Graph attention networks, in: Proceedings of ICLR, 2018. Jie Chen, Tengfei Ma, Cao Xiao, FastGCN: Fast learning with graph convolutional networks via importance sampling, in: Proceedings of ICLR, 2018. Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, Cho-Jui Hsieh, Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks, in: Proceedings of KDD, 2019. Jianfei Chen, Jun Zhu, Le Song, Stochastic training of graph convolutional networks with variance reduction, in: Proceedings of ICML, 2018, pp. 941–949. Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, Xu Sun, Measuring and relieving the over-smoothing problem for graph neural networks from the topological view, in: Proceedings of AAAI, 2020. Qimai Li, Zhichao Han, Xiao-Ming Wu, Deeper insights into graph convolutional networks for semi-supervised learning, in: Proceedings of AAAI, 2018. Hector Martinez Alonso, Barbara Plank, When is multitask learning effective? Semantic sequence prediction under varying data conditions, in: Proceedings of EACL, 2017, pp. 1–10. Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang, Personalized microblog sentiment classification via adversarial cross-lingual multi-task learning, in: Proceedings of EMNLP, 2018, pp. 338–348. Chen, 2019, Co-attentive multi-task learning for explainable recommendation, 2137 Wenao Ma, Shuang Yu, Kai Ma, Jiexiang Wang, Xinghao Ding, Yefeng Zheng, Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification, in: Proceedings of MICCAI, 2019, pp. 769–778. Chester Holtz, Onur Atan, Ryan Carey, Tushit Jain, Multi-task learning on graphs with node and graph level labels, in: Proceedings of NeurIPS, 2019. Xie, 2020, A multi-task representation learning architecture for enhanced graph classification, Front. Neurosci., 13, 1395, 10.3389/fnins.2019.01395 Avelar, 2019, Multitask learning on graph neural networks: Learning multiple graph centrality measures with a unified network, 701 Phi Vu Tran, Multi-task graph autoencoders, in: Proceedings of NeurIPS Workshop on Relational Representation Learning, 2018. Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, Andrew Rabinovich, GradNorm: Gradient Normalization for adaptive loss balancing in deep multitask networks, in: Proceedings of ICML, 2018, pp. 793–802. Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Pin Xing, Autoloss: Learning discrete schedules for alternate optimization, in: Proceedings of ICLR, 2019. Sébastien Jean, Orhan Firat, Melvin Johnson, Adaptive scheduling for multi-task learning, in: Proceedings of NeurIPS Workshop on Continual Learning, 2018. Shikun Liu, Edward Johns, Andrew J. Davison, End-to-end multi-task learning with attention, in: Proceedings of CVPR, 2019, pp. 1871–1880. Karypis, 1998, A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J. Sci. Comput., 20, 359, 10.1137/S1064827595287997 Sen, 2008, Collective classification in network data, AI Mag., 29, 93 Wang, 2019 Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala, PyTorch: An imperative style, high-performance deep learning library, in: Proceedings of NeurIPS, 2019, pp. 8024–8035. Diederik P. Kingma, Jimmy Ba, Adam: A method for stochastic optimization, in: Proceedings of ICLR, 2015. Prechelt, 1998, Early stopping - but when?, 55 Srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Hand, 2012, Assessing the performance of classification methods, Internat. Statist. Rev., 80, 10.1111/j.1751-5823.2012.00183.x Cèsar Ferri, Peter A. Flach, José Hernández-Orallo, Modifying ROC curves to incorporate predicted probabilities, in: Proceedings of ICML Workshops on ROC Analysis in Machine Learning, 2005.