Acemoglu, D, Asuman O, Alireza T-S (2015) Systemic Risk and Stability in Financial Networks. Am Econ Rev 105(2):564–608. https://doi.org/10.1257/aer.20130456. https://www.aeaweb.org/articles?id=10.1257/aer.20130456.

Article
Google Scholar

Altman, EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609.

Article
Google Scholar

Amihud, Y (2002) Illiquidity and stock returns: cross-section and time-series effects. J Financ Mark 5(1):31–56.

Article
Google Scholar

Bardoscia, M, Stefano B, Fabio C, Caldarelli G (2017) Pathways towards instability in financial networks. Nat Commun 8:14416. https://doi.org/10.1038/ncomms14416. https://www.nature.com/articles/ncomms14416.

Article
Google Scholar

Barrat, A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci U S A 101(11):3747–3752. https://doi.org/10.1073/pnas.0400087101. http://www.pnas.org/content/101/11/3747.

Article
Google Scholar

Batagelj, V, Zaveršnik M (2011) Fast algorithms for determining (generalized) core groups in social networks. ADAC 5(2):129–145. https://doi.org/10.1007/s11634-010-0079-y. https://doi.org/10.1007/s11634-010-0079-y.

Article
MathSciNet
MATH
Google Scholar

Belkin, M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering In: Advances in neural information processing systems, 585–591.. MIT Press, Cambridge.

Google Scholar

Belkin, M, Niyogi P (2004) Semi-supervised learning on riemannian manifolds. Mach Learn 56(1-3):209–239.

Article
MATH
Google Scholar

Benami, I, Cohen K, Nagar O, Louzoun Y (2019) Topological based classification of paper domains using graph convolutional networks. arXiv:1904.07787 [cs, stat]. arXiv: 1904.07787. http://arxiv.org/abs/1904.07787.

Boers, N, Bookhagen B, Barbosa HMJ, Marwan N, Kurths J, Marengo JA (2014) Prediction of extreme floods in the eastern Central Andes based on a complex networks approach. Nat Commun 5:5199. https://doi.org/10.1038/ncomms6199. http://www.nature.com/doifinder/10.1038/ncomms6199.

Article
Google Scholar

Breiman, L (2001) Random Forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324.

Article
MATH
Google Scholar

Brockmann, D, Helbing D (2013) The Hidden Geometry of Complex, Network-Driven Contagion Phenomena. Science 342(6164):1337–1342. https://doi.org/10.1126/science.1245200. http://science.sciencemag.org/content/342/6164/1337.

Article
Google Scholar

Bruna, J, Zaremba Wojciech, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203.

Carmi, S, Havlin S, Kirkpatrick S, Shavitt Y, Shir E (2007) A model of Internet topology using k-shell decomposition. Proc Natl Acad Sci 104(27):11150–11154. https://doi.org/10.1073/pnas.0701175104. http://www.pnas.org/content/104/27/11150.

Article
Google Scholar

Cohen, R, Havlin S (2010) Complex networks: structure, robustness and function. Cambridge university press, Cambridge.

Book
MATH
Google Scholar

Colizza, V, Barrat A, Barthélemy M, Vespignani A (2006) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci U S A 103(7):2015–2020.

Article
MATH
Google Scholar

Cooper, MJ, Gulen H, Schill MJ (2008) Asset growth and the cross-section of stock returns. J Finance 63(4):1609–1651.

Article
Google Scholar

Dijkstra, EW (1959) A Note on Two Problems in Connexion with Graphs. Numer Math 1(1):269–271. https://doi.org/10.1007/BF01386390. http://dx.doi.org/10.1007/BF01386390.

Article
MathSciNet
MATH
Google Scholar

Duvenaud, DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints In: Advances in neural information processing systems, 2224–2232.. MIT Press, Cambridge.

Google Scholar

Eubank, S, Guclu H, Kumar VSA, Marathe MV, Srinivasan A, Toroczkai Z, Wang N (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429(6988):180. https://doi.org/10.1038/nature02541. https://www.nature.com/articles/nature02541.

Article
Google Scholar

Everett, MG, Borgatti SP (1999) The centrality of groups and classes. J Math Sociol 23(3):181–201. https://doi.org/10.1080/0022250X.1999.9990219.

Article
MATH
Google Scholar

Fama, EF, French KR (1992) The cross-section of expected stock returns. J Finance 47(2):427–465.

Article
Google Scholar

Grover, A, Leskovec J (2016) Node2vec: Scalable Feature Learning for Networks In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, 855–864.. ACM, New York. https://doi.org/10.1145/2939672.2939754. event-place: San Francisco, California, USA. ISBN 978-1-4503-4232-2. http://doi.acm.org/10.1145/2939672.2939754.

Chapter
Google Scholar

Ho, TK (1995) Random decision forests In: Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1, 278–2821. https://doi.org/10.1109/ICDAR.1995.598994.

Hoberg, G, Phillips G (2010) Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis. Rev Financ Stud 23(10):3773–3811. https://doi.org/10.1093/rfs/hhq053. http://rfs.oxfordjournals.org/content/23/10/3773.

Article
Google Scholar

Hoberg, G, Phillips G (2016) Text-based network industries and endogenous product differentiation. J Polit Econ 124(5):1423–1465. https://doi.org/10.1086/688176.

Article
Google Scholar

Itzhack, R, Mogilevski Y, Louzoun Y (2007) An optimal algorithm for counting network motifs. Phys A Stat Mech Appl 381:482–490. https://doi.org/10.1016/j.physa.2007.02.102. http://www.sciencedirect.com/science/article/pii/S0378437107002257.

Article
Google Scholar

Kingma, DP, Ba J (2014) Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs]. arXiv: 1412.6980. http://arxiv.org/abs/1412.6980.

Kipf, TN, Welling M (2016a) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.

Kipf, TN, Welling M (2016b) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308.

Kitsak, M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893. https://doi.org/10.1038/nphys1746. http://www.nature.com/nphys/journal/v6/n11/full/nphys1746.html.

Article
Google Scholar

Levy, O, Goldberg Y, Dagan I (2015) Improving distributional similarity with lessons learned from word embeddings. Trans Assoc Comput Linguist 3:211–225.

Article
Google Scholar

Li, R, Dong L, Zhang J, Wang X, Wang W-X, Di Z, Stanley HE (2017) Simple spatial scaling rules behind complex cities. Nat Commun 8(1):1841. https://doi.org/10.1038/s41467-017-01882-w. https://www.nature.com/articles/s41467-017-01882-w.

Article
Google Scholar

Ludescher, J, Gozolchiani A, Bogachev MI, Bunde A, Havlin S, Schellnhuber HJ (2014) Very early warning of next el nino. Proc Natl Acad Sci 111(6):2064–2066. https://doi.org/10.1073/pnas.1323058111. http://www.pnas.org/content/111/6/2064.

Google Scholar

Masci, J, Boscaini D, Bronstein M, Vandergheynst P (2015) Shapenet: Convolutional neural networks on non-euclidean manifolds. Technical report.

Meng, J, Fan J, Ashkenazy Y, Havlin S (2017) Percolation framework to describe El Nino conditions. Chaos Interdisc J Nonlinear Sci 27(3):035807. https://doi.org/10.1063/1.4975766. http://aip.scitation.org/doi/abs/10.1063/1.4975766.

Article
Google Scholar

Meng, J, Fan J, Ashkenazy Y, Bunde A, Havlin S (2018) Forecasting the magnitude and onset of El Niño based on climate network. New J Phys 20(4):043036. https://doi.org/10.1088/1367-2630/aabb25. http://stacks.iop.org/1367-2630/20/i=4/a=043036.

Article
Google Scholar

Milo, R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network Motifs: Simple Building Blocks of Complex Networks. Science 298(5594):824–827. https://doi.org/10.1126/science.298.5594.824. http://science.sciencemag.org/content/298/5594/824.

Article
Google Scholar

Miura, W, Takayasu H, Takayasu M (2012) Effect of Coagulation of Nodes in an Evolving Complex Network. Phys Rev Lett 108(16):168701. https://doi.org/10.1103/PhysRevLett.108.168701. https://link.aps.org/doi/10.1103/PhysRevLett.108.168701.

Article
Google Scholar

Monti, F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model cnns In: Proc. cvpr, Vol. 1, 3.. IEEE, New York.

Google Scholar

Naaman, R, Cohen K, Louzoun Y (2018) Edge sign prediction based on a combination of network structural topology and sign propagation. J Complex Netw 7(1):54–66. https://doi.org/10.1093/comnet/cny012. https://academic.oup.com/comnet/advance-article/doi/10.1093/comnet/cny012/4999727.

Article
MathSciNet
Google Scholar

Newman, M (2010) Networks: an introduction. Oxford university press, Oxford.

Book
MATH
Google Scholar

Perozzi, B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations In: Proceedings of the 20th acm sigkdd international conference on knowledge discovery and data mining, 701–710.. ACM, New York.

Google Scholar

Rosen, Y, Louzoun Y (2014) Directionality of real world networks as predicted by path length in directed and undirected graphs. Phys A Stat Mech Appl 401:118–129. https://doi.org/10.1016/j.physa.2014.01.005. http://www.sciencedirect.com/science/article/pii/S0378437114000090.

Article
MathSciNet
MATH
Google Scholar

Rosen, Y, Louzoun Y (2015) Topological similarity as a proxy to content similarity. J Complex Netw 4(1):38–60.

Article
MathSciNet
Google Scholar

Rosen, Y, Louzoun Y (2016) Topological similarity as a proxy to content similarity. J Complex Netw 4(1):38–60. https://doi.org/10.1093/comnet/cnv012. https://academic.oup.com/comnet/article/4/1/38/2366087.

Article
MathSciNet
Google Scholar

Rosenfeld, N, Globerson A (2017) Semi-supervised learning with competitive infection models. arXiv preprint arXiv:1703.06426.

Sabidussi, G (1966) The centrality index of a graph. Psychometrika 31(4):581–603. https://doi.org/10.1007/BF02289527. https://doi.org/10.1007/BF02289527.

Article
MathSciNet
MATH
Google Scholar

Saramäki, J, Kivelä M, Onnela J-P, Kaski K, Kertész J (2007) Generalizations of the clustering coefficient to weighted complex networks. Phys Rev E 75(2):027105. https://doi.org/10.1103/PhysRevE.75.027105. https://link.aps.org/doi/10.1103/PhysRevE.75.027105.

Article
Google Scholar

Schlichtkrull, M, Kipf TN, Bloem P, van den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks In: European semantic web conference, 593–607.. Springer, Berlin.

Chapter
Google Scholar

Sinatra, R, Wang D, Deville P, Song C, Barabási A-L (2016) Quantifying the evolution of individual scientific impact. Science 354(6312):5239. https://doi.org/10.1126/science.aaf5239. http://science.sciencemag.org/content/354/6312/aaf5239.

Article
Google Scholar

Sindhwani, V, Partha N, Belkin M (2005) Beyond the point cloud: from transductive to semi-supervised learning In: Proceedings of the 22nd international conference on machine learning, 824–831.. ACM, New York.

Google Scholar

Watts, DJ, Strogatz SH (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):440–442. https://doi.org/10.1038/30918. http://www.nature.com/nature/journal/v393/n6684/full/393440a0.html.

Article
MATH
Google Scholar

Yang, Z, Cohen WW, Salakhutdinov R (2016) Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861.

Zhao, J-H, Zhou H-J, Liu Y-Y (2013) Inducing effect on the percolation transition in complex networks. Nat Commun 4:2412. https://doi.org/10.1038/ncomms3412. http://www.nature.com/ncomms/2013/130909/ncomms3412/full/ncomms3412.html.

Article
Google Scholar

Zhou, D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency In: Advances in neural information processing systems, 321–328.. MIT Press, Cambridge.

Google Scholar

Zhu, X, Ghahramani Z, Lafferty JD (2003) Semi-supervised learning using gaussian fields and harmonic functions In: Proceedings of the 20th international conference on machine learning (icml-03), 912–919.. ACM, New York.

Google Scholar