of Words and Phrases and Their Compositionality.” Advances in Neural Information Processing Systems 26, 2013, pp. 3111–3119. Bengio, Y., et al. “Representation Learning: A Review and New Perspectives.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, 2013, pp. 1798–1828. Vol.31.No.4(2016/7)ネットワークの表現学習 – 人工知能学会 https://www.ai-gakkai.or.jp/my-bookmark_vol31-no4/ 言語と画像の表現学習 https://www.slideshare.net/yukinoguchi999/ss-59238906
and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014, pp. 701–710. (Tang+ 2015) J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th international conference on world wide web. International World Wide Web Conferences Steering Committee, 2015, pp. 1067–1077. (Grover&Leskovec 2016) A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016, pp. 855–864. (Shervashidze+ 2011) Shervashidze, Nino, et al. “Weisfeiler-Lehman Graph Kernels.” Journal of Machine Learning Research, vol. 12, 2011, pp. 2539–2561.
et al. “Spectral Networks and Locally Connected Networks on Graphs.” ICLR 2014 : International Conference on Learning Representations (ICLR) 2014, 2014. ChebNet (Defferrard+ 2016) Defferrard, Michaël, et al. “Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering.” NIPS’16 Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016, pp. 3844–3852. Graph Convolutional Network (Kipf&Welling 2017) Kipf, Thomas N., and Max Welling. “Semi-Supervised Classification with Graph Convolutional Networks.” ICLR 2017 : International Conference on Learning Representations 2017, 2017. Message Passing Neural Network (Gilmer+ 2017) Gilmer, Justin, et al. “Neural Message Passing for Quantum Chemistry.” ICML’17 Proceedings of the 34th International Conference on Machine Learning - Volume 70, 2017, pp. 1263–1272. Neural Networks for Graph (Micheli 2009) Micheli, A. “Neural Network for Graphs: A Contextual Constructive Approach.” IEEE Transactions on Neural Networks, vol. 20, no. 3, 2009, pp. 498–511. DCNN (Atwood&Towsley 2016) Atwood, James, and Don Towsley. “Diffusion-Convolutional Neural Networks.” Advances in Neural Information Processing Systems, 2016, pp. 1993–2001. GAT (Velickovic+ 2018) P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” ICLR 2018, 2018.
N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in ESWC 2018 MoNet (Monti+ 2017) F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model cnns,” CVPR 2017, pp. 5425–5434, 2017. GraphSAGE (Hamilton+ 2017) Hamilton, William L., et al. “Inductive Representation Learning on Large Graphs.” Advances in Neural Information Processing Systems, 2017, pp. 1024– 1034. DiffPool (Ying+ 2018) Ying, Zhitao, et al. “Hierarchical Graph Representation Learning with Differentiable Pooling.” NIPS 2018: The 32nd Annual Conference on Neural Information Processing Systems, 2018, pp. 4805–4815. NRI (Kipf+ 2018) Kipf, Thomas, et al. “Neural Relational Inference for Interacting Systems.” ICML 2018: Thirty-Fifth International Conference on Machine Learning, 2018, pp. 2688–2697. GIN (Xu+2019) Xu, Keyulu, et al. “How Powerful Are Graph Neural Networks.” ICLR 2019 : 7th International Conference on Learning Representations, 2019.
Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi- supervised learning. In Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. (Morris+ 2019) Morris, Christopher, et al. “Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks.” AAAI 2019 : Thirty-Third AAAI Conference on Artificial Intelligence, vol. 33, no. 1, 2019, pp. 4602–4609. (Xu+ 2019) Xu, Keyulu, et al. “How Powerful Are Graph Neural Networks.” ICLR 2019 : 7th International Conference on Learning Representations, 2019. (Dehmamy+ 2019) Dehmamy, Nima, et al. “Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology.” NeurIPS 2019 : Thirty-Third Conference on Neural Information Processing Systems, 2019, pp. 15387–15397.
Berkeley: AI-Sys Spring 2019 https://ucbrise.github.io/cs294-ai-sys-sp19/ EPFL: A Network Tour of Data Science https://github.com/mdeff/ntds_2019 Master Seminar "Deep Learning for Graphs" / "Recent Developments in Data Science" (WS 2019/20) https://www.dbs.ifi.lmu.de/cms/studium_lehre/lehre_master/semrecent1920/index.html