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Principles of animal ecology. Technical Report. Saunders Company Philadelphia , Pennsylvania, USA. Warder Clyde Allee, Orlando Park, Alfred Edwards Emerson, Thomas Park, Karl Patterson Schmidt, et al. 1949. Principles of animal ecology. Technical Report. Saunders Company Philadelphia, Pennsylvania, USA."},{"key":"e_1_3_2_2_2_1","volume-title":"An introduction to systems biology: design principles of biological circuits","author":"Alon Uri","unstructured":"Uri Alon . 2006. An introduction to systems biology: design principles of biological circuits . Chapman and Hall\/CRC. Uri Alon. 2006. An introduction to systems biology: design principles of biological circuits .Chapman and Hall\/CRC."},{"key":"e_1_3_2_2_3_1","volume-title":"Emergence of scaling in random networks. science","author":"Barab\u00e1si Albert-L\u00e1szl\u00f3","year":"1999","unstructured":"Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert . 1999. Emergence of scaling in random networks. science , Vol. 286 , 5439 ( 1999 ), 509--512. Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert. 1999. Emergence of scaling in random networks. science, Vol. 286, 5439 (1999), 509--512."},{"key":"e_1_3_2_2_4_1","volume-title":"Constructing minimal models for complex system dynamics. Nature communications","author":"Barzel Baruch","year":"2015","unstructured":"Baruch Barzel , Yang-Yu Liu , and Albert-L\u00e1szl\u00f3 Barab\u00e1si . 2015. Constructing minimal models for complex system dynamics. Nature communications ( 2015 ). Baruch Barzel, Yang-Yu Liu, and Albert-L\u00e1szl\u00f3 Barab\u00e1si. 2015. Constructing minimal models for complex system dynamics. 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Elementary differential equations and boundary value problems . Vol. 9 . Wiley New York . William E Boyce, Richard C DiPrima, and Douglas B Meade. 1992. Elementary differential equations and boundary value problems. Vol. 9. Wiley New York."},{"key":"e_1_3_2_2_8_1","unstructured":"Tian Qi Chen Yulia Rubanova Jesse Bettencourt and David K Duvenaud. 2018. Neural ordinary differential equations. In Advances in Neural Information Processing Systems. 6571--6583.  Tian Qi Chen Yulia Rubanova Jesse Bettencourt and David K Duvenaud. 2018. Neural ordinary differential equations. In Advances in Neural Information Processing Systems. 6571--6583."},{"key":"e_1_3_2_2_9_1","volume-title":"Numerical methods for differential equations: a computational approach","author":"Dormand John R","unstructured":"John R Dormand . 1996. Numerical methods for differential equations: a computational approach . Vol. 3 . CRC Press . John R Dormand. 1996. Numerical methods for differential equations: a computational approach. Vol. 3. CRC Press."},{"key":"e_1_3_2_2_10_1","volume-title":"Augmented neural odes. arXiv preprint arXiv:1904.01681","author":"Dupont Emilien","year":"2019","unstructured":"Emilien Dupont , Arnaud Doucet , and Yee Whye Teh . 2019. Augmented neural odes. arXiv preprint arXiv:1904.01681 ( 2019 ). Emilien Dupont, Arnaud Doucet, and Yee Whye Teh. 2019. Augmented neural odes. arXiv preprint arXiv:1904.01681 (2019)."},{"key":"e_1_3_2_2_11_1","volume-title":"Publ. Math. Debrecen","volume":"6","author":"Erdos P","year":"1959","unstructured":"P Erdos and A Renyi . 1959 . On random graphs I . Publ. Math. Debrecen , Vol. 6 (1959). P Erdos and A Renyi. 1959. On random graphs I. Publ. Math. Debrecen, Vol. 6 (1959)."},{"key":"e_1_3_2_2_12_1","volume-title":"Community detection in graphs. Physics reports","author":"Fortunato Santo","year":"2010","unstructured":"Santo Fortunato . 2010. Community detection in graphs. 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Zachary C Lipton, John Berkowitz, and Charles Elkan. 2015. A critical review of recurrent neural networks for sequence learning. preprint arXiv:1506.00019 (2015)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00045"},{"key":"e_1_3_2_2_25_1","volume-title":"Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations. arXiv preprint arXiv:1710.10121","author":"Lu Yiping","year":"2017","unstructured":"Yiping Lu , Aoxiao Zhong , Quanzheng Li , and Bin Dong . 2017. Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations. arXiv preprint arXiv:1710.10121 ( 2017 ). Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong. 2017. Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations. arXiv preprint arXiv:1710.10121 (2017)."},{"key":"e_1_3_2_2_26_1","volume-title":"Analytical heat diffusion theory","author":"Luikov A","unstructured":"A v Luikov . 2012. Analytical heat diffusion theory . Elsevier . A v Luikov. 2012. Analytical heat diffusion theory .Elsevier."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMBMC.2016.2633265"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2018.03.074"},{"key":"e_1_3_2_2_29_1","volume-title":"Networks: an introduction","author":"Newman Mark","unstructured":"Mark Newman . 2010. Networks: an introduction . Oxford U. press . Mark Newman. 2010. Networks: an introduction .Oxford U. press."},{"key":"e_1_3_2_2_30_1","volume-title":"The structure and dynamics of networks","author":"Newman Mark","unstructured":"Mark Newman , Albert-Laszlo Barabasi , and Duncan J Watts . 2011. 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Multistep neural networks for data-driven discovery of nonlinear dynamical systems. arXiv preprint arXiv:1801.01236 (2018)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.1602614"},{"key":"e_1_3_2_2_35_1","volume-title":"Deep neural networks motivated by partial differential equations. arXiv preprint arXiv:1804.04272","author":"Ruthotto Lars","year":"2018","unstructured":"Lars Ruthotto and Eldad Haber . 2018. Deep neural networks motivated by partial differential equations. arXiv preprint arXiv:1804.04272 ( 2018 ). Lars Ruthotto and Eldad Haber. 2018. Deep neural networks motivated by partial differential equations. arXiv preprint arXiv:1804.04272 (2018)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"e_1_3_2_2_37_1","unstructured":"Jean-Jacques E Slotine Weiping Li etal 1991. Applied nonlinear control. Vol. 199. Prentice hall Englewood Cliffs NJ.  Jean-Jacques E Slotine Weiping Li et al. 1991. 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Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735 (2018)."},{"key":"e_1_3_2_2_40_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_41_1","volume-title":"Collective dynamics of \"mall-world\" networks. nature","author":"Watts Duncan J","year":"1998","unstructured":"Duncan J Watts and Steven H Strogatz . 1998. Collective dynamics of \"mall-world\" networks. nature , Vol. 393 , 6684 ( 1998 ), 440. Duncan J Watts and Steven H Strogatz. 1998. Collective dynamics of \"mall-world\" networks. nature, Vol. 393, 6684 (1998), 440."},{"key":"e_1_3_2_2_42_1","volume-title":"2019 b. Simplifying Graph Convolutional Networks. CoRR","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Tianyi Zhang , Amauri H. Souza Jr ., Christopher Fifty , Tao Yu , and Kilian Q. Weinberger . 2019 b. Simplifying Graph Convolutional Networks. CoRR ( 2019 ). Felix Wu, Tianyi Zhang, Amauri H. Souza Jr., Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019 b. Simplifying Graph Convolutional Networks. CoRR (2019)."},{"key":"e_1_3_2_2_43_1","volume-title":"2019 a. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu , Shirui Pan , Fengwen Chen , Guodong Long , Chengqi Zhang , and Philip S Yu . 2019 a. A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 ( 2019 ). 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