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Network Structure Inference, A Survey: Motivations, Methods, and Applications

Authors:

Brian Gallagher,

Tanya Y. Berger-WolfAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 2

Article No.: 24, Pages 1 - 39

https://doi.org/10.1145/3154524

Published: 17 April 2018 Publication History

Abstract

Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users “friends” in a social network? Do two researchers collaborate on a published article? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationships between nodes are not directly observable and must be inferred: Does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population?

Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology that employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network’s capability of answering questions on the system of interest.

References

[1]

Eytan Adar and Lada A. Adamic. 2005. Tracking information epidemics in blogspace. In Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence. 207--214.

Digital Library

[2]

Charu Aggarwal and Karthik Subbian. 2014. Evolutionary network analysis: A survey. ACM Comput. Surv. 47, 1 (May 2014), 10:1--10:36.

Digital Library

[3]

Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, and Alex Pentland. 2011. Social fMRI: Investigating and shaping social mechanisms in the real world. Pervas. Mobil. Comput. 7, 6 (Dec. 2011), 643--659.

Digital Library

[4]

Genevera I. Allen and Zhandong Liu. 2012. A log-linear graphical model for inferring genetic networks from high-throughput sequencing data. In Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM’12). IEEE Computer Society, 1--6.

Digital Library

[5]

Lucy M. Aplin, Damien R. Farine, Julie Morand-Ferron, Andrew Cockburn, Alex Thornton, and Ben C. Sheldon. 2015. Experimentally induced innovations lead to persistent culture via conformity in wild birds. Nature 518, 7540 (Feb. 2015), 538--541.

[6]

Lucy M. Aplin, Damien R. Farine, Julie Morand-Ferron, and Ben C. Sheldon. 2012. Social networks predict patch discovery in a wild population of songbirds. Proceedings of the Royal Society of London B: Biological Sciences (2012).

[7]

Mahmoudreza Babaei, Przemyslaw Grabowicz, Isabel Valera, Krishna P. Gummadi, and Manuel Gomez-Rodriguez. 2016. On the efficiency of the information networks in social media. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM’16). ACM, 83--92.

Digital Library

[8]

Lars Backstrom and Jon Kleinberg. 2011. Network bucket testing. In Proceedings of the 20th International Conference on World Wide Web (WWW’11). ACM, 615--624.

Digital Library

[9]

Shweta Bansal, Bryan T. Grenfell, and Lauren Ancel Meyers. 2007. When individual behaviour matters: Homogeneous and network models in epidemiology. J. Roy. Soc. Interface 4, 16 (2007), 879--891.

[10]

Ziv Bar-Joseph, Anthony Gitter, and Itamar Simon. 2012. Studying and modelling dynamic biological processes using time-series gene expression data. Nature Rev. Genet. 13, 8 (Aug. 2012), 552--564.

[11]

Alain Barrat, Ciro Cattuto, Vittoria Colizza, Jean-François Pinton, Wouter Van den Broeck, and Alessandro Vespignani. 2008. High resolution dynamical mapping of social interactions with active RFID. ArXiv e-prints (Nov. 2008). http://arxiv.org/abs/0811.4170v2

[12]

Louise Barrett, Peter Henzi, and David Lusseau. 2012. Taking sociality seriously: The structure of multi-dimensional social networks as a source of information for individuals. Philos. Trans. Roy. Soc. London B: Biol. Sci. 367, 1599 (2012), 2108--2118.

[13]

Tanya Barrett, Stephen E. Wilhite, Pierre Ledoux, Carlos Evangelista, Irene F. Kim, Maxim Tomashevsky, Kimberly A. Marshall, Katherine H. Phillippy, Patti M. Sherman, Michelle Holko, Andrey Yefanov, Hyeseung Lee, Naigong Zhang, Cynthia L. Robertson, Nadezhda Serova, Sean Davis, and Alexandra Soboleva. 2013. NCBI GEO: Archive for functional genomics data sets--update. Nucleic Acids Res. 41, D1 (2013), D991--D995.

[14]

Baruch Barzel and Albert-Laszlo Barabasi. 2013. Network link prediction by global silencing of indirect correlations. Nature Biotechnol. 31, 8 (Aug. 2013), 720--725.

[15]

Lars Bejder, David Fletcher, and Stefan Bräger. 1998. A method for testing association patterns of social animals. Animal Behav. 56, 3 (1998), 719--725.

[16]

David C. Bell, John S. Atkinson, and Jerry W. Carlson. 1999. Centrality measures for disease transmission networks. Social Networks 21, 1 (1999), 1--21.

[17]

Tanya Y. Berger-Wolf, Daniel I. Rubenstein, Charles V. Stewart, Jason A. Holmberg, Jason Parham, Sreejith Menon, Jonathan Crall, Jon Van Oast, Emre Kiciman, and Lucas Joppa. 2017. Wildbook: Crowdsourcing, computer vision, and data science for conservation. ArXiv e-prints (Oct. 2017). https://arxiv.org/abs/1710.08880

[18]

Stephan Bialonski, Martin Wendler, and Klaus Lehnertz. 2011. Unraveling spurious properties of interaction networks with tailored random networks. PLoS ONE 6, 8 (Aug. 2011), e22826.

[19]

Concha Bielza and Pedro Larrañaga. 2014. Bayesian networks in neuroscience: A survey. Front. Comput. Neurosci. 8 (Oct. 2014), 131.

[20]

Vincent D. Blondel, Adeline Decuyper, and Gautier Krings. 2015. A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4, 1 (2015), 1--55.

[21]

Vincent D. Blondel, Gautier Krings, and Isabelle Thomas. 2010. Regions and borders of mobile telephony in belgium and in the brussels metropolitan zone. Brussels Studies (2010). https://doi.org/2078.1/95261

[22]

Stefano Boccaletti, Ginestra Bianconi, Regino Criado Herrero, Charo I. del Genio, Jesus Gómez-Gardeñes, Miguel Romance, Irene Sendiña-Nadal, Zhen Wang, and Massimiliano Zanin. 2014. The structure and dynamics of multilayer networks. Phys. Rep. 544, 1 (2014), 1--122.

[23]

Michael Boots and Akira Sasaki. 1999. “Small worlds” and the evolution of virulence: Infection occurs locally and at a distance. Philos. Trans. Roy. Soc. London B: Biol. Sci. 266, 1432 (1999), 1933--1938.

[24]

Ulrik Brandes, Garry Robins, Ann Mccranie, and Stanley Wasserman. 2013. What is network science? Network Sci. 1, 1 (2013), 1--15.

[25]

Fred Brauer. 2008. Compartmental Models in Epidemiology. Springer, Berlin, 19--79.

[26]

Tom Britton and Philip D. O’Neill. 2002. Bayesian inference for stochastic epidemics in populations with random social structure. Scand. J. Stat. 29, 3 (Sept. 2002), 375--390.

[27]

Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng. 2011. Handbook of Markov Chain Monte Carlo. CRC Press. 2011021891 https://books.google.com/books?id=qfRsAIKZ4rIC.

[28]

Ivan Brugere and Tanya Y. Berger-Wolf. 2017. Network model selection using task-focused minimum description length. ArXiv e-prints (Oct. 2017). https://arxiv.org/abs/1710.05207

[29]

Ivan Brugere, Chris Kanich, and Tanya Y. Berger-Wolf. 2017. Evaluating social networks using task-focused network inference. In Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG’17). https://arxiv.org/abs/1707.02385

[30]

Ivan Brugere, Chris Kanich, and Tanya Y. Berger-Wolf. 2017. Network model selection for task-focused attributed network inference. In Proceedings of the 2017 IEEE International Conference on Data Mining Workshop (ICDMW’17). https://arxiv.org/abs/1708.06303

[31]

Ed Bullmore and Olaf Sporns. 2009. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 3 (Mar. 2009), 186--198.

[32]

Atul J. Butte and Isaac S. Kohane. 2000. Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. Pacific Symp. Biocomput. 5 (2000), 415--426.

[33]

Leila Cammoun, Xavier Gigandet, Djalel Meskaldji, Jean Philippe Thiran, Olaf Sporns, Kim Q. Do, Philippe Maeder, Reto Meuli, and Patric Hagmann. 2012. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 2 (2012), 386--397.

[34]

Bokai Cao, Lifang He, Xiaokai Wei, Mengqi Xing, Philip S. Yu, Heide Klumpp, and Alex D. Leow. 2017. t-BNE: Tensor-based Brain Network Embedding. 189--197.

[35]

Longbing Cao. 2017. Data science: A comprehensive overview. ACM Comput. Surv. 50, 3, Article 43 (June 2017), 42 pages.

Digital Library

[36]

Ciro Cattuto, Wouter Van den Broeck, Alain Barrat, Vittoria Colizza, Jean-François Pinton, and Alessandro Vespignani. 2010. Dynamics of person-to-person interactions from distributed RFID sensor networks. PLOS ONE 5, 7 (July 2010), e11596.

[37]

Hau Chan, Hanghang Tong, and Leman Akoglu. 2014. Make it or break it: Manipulating robustness in large networks. In Proceedings of the 2014 SIAM International Conference on Data Mining. 325--333.

[38]

Darren P. Croft, Jens Krause, Safi K. Darden, Indar W. Ramnarine, Jolyon J. Faria, and Richard James. 2009. Behavioural trait assortment in a social network: Patterns and implications. Behav. Ecol. Sociobiol. 63, 10 (2009), 1495--1503.

[39]

Darren P. Croft, Joah R. Madden, Daniel W. Franks, and Richard James. 2011. Hypothesis testing in animal social networks. Trends Ecol. Evol. 26, 10 (2011), 502--507.

[40]

Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2017. A survey on network embedding. ArXiv e-prints (Nov. 2017). https://arxiv.org/abs/1711.08752

[41]

Luciano da Fontoura Costa, Osvaldo N. Oliveira Jr., Gonzalo Travieso, Francisco Aparecido Rodrigues, Paulino Ribeiro Villas Boas, Lucas Antiqueira, Matheus Palhares Viana, and Luis Enrique Correa Rocha. 2011. Analyzing and modeling real-world phenomena with complex networks: A survey of applications. Adv. Phys. 60, 3 (2011), 329--412.

[42]

E. Damaraju, E. A. Allen, A. Belger, J. M. Ford, S. McEwen, D. H. Mathalon, B. A. Mueller, G. D. Pearlson, S. G. Potkin, A. Preda, J. A. Turner, J. G. Vaidya, T. G. van Erp, and V. D. Calhoun. 2014. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clin. 5 (2014), 298--308.

[43]

Leon Danon, Ashley P. Ford, Thomas House, Chris P. Jewell, Matt J. Keeling, Gareth O. Roberts, Joshua V. Ross, and Matthew C. Vernon. 2011. Networks and the epidemiology of infectious disease. In Interdisciplinary Perspectives on Infectious Diseases, Vol. 2011.

[44]

Olivier David, Isabelle Guillemain, Sandrine Saillet, Sebastien Reyt, Colin Deransart, Christoph Segebarth, and Antoine Depaulis. 2008. Identifying neural drivers with functional MRI: An electrophysiological validation. PLoS Biol. 6, 12 (Dec. 2008), e315.

[45]

Munmun De Choudhury, Winter A. Mason, Jake M. Hofman, and Duncan J. Watts. 2010. Inferring relevant social networks from interpersonal communication. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, 301--310.

Digital Library

[46]

Yves-Alexandre de Montjoye, César A. Hidalgo, Michel Verleysen, and Vincent D. Blondel. 2013. Unique in the crowd: The privacy bounds of human mobility. Nature Sci. Rep. 3 (Mar 2013), 1376.

[47]

Shermin de Silva, Ashoka D. G. Ranjeewa, and Sergey Kryazhimskiy. 2011. The dynamics of social networks among female Asian elephants. BMC Ecol. 11, 1 (2011), 1--16.

[48]

Mukeshwar Dhamala, Govindan Rangarajan, and Mingzhou Ding. 2008. Analyzing information flow in brain networks with nonparametric granger causality. NeuroImage 41, 2 (2008), 354--362.

[49]

Jonathan F. Donges, Yong Zou, Norbert Marwan, and Juergen Kurths. 2009. Complex networks in climate dynamics. Eur. Phys. J. Special Topics 174, 1 (2009), 157--179.

[50]

Jonathan F. Donges, Yong Zou, Norbert Marwan, and Juergen Kurths. 2009. The backbone of the climate network. Europhys. Lett. 87, 4 (2009), 48007.

[51]

Nan Du, Le Song, Ming Yuan, and Alex J. Smola. 2012. Learning networks of heterogeneous influence. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). 2780--2788. http://papers.nips.cc/paper/4582-learning-networks-of-heterogeneous-influence.

Digital Library

[52]

Robin M. Dunbar. 1992. Time: A hidden constraint on the behavioural ecology of baboons. Behav. Ecol. Sociobiol. 31, 1 (01 Jul 1992), 35--49.

[53]

Nathan Eagle and Alex Sandy Pentland. 2009. Eigenbehaviors: Identifying structure in routine. Behav. Ecol. Sociobiol. 63, 7 (2009), 1057--1066.

[54]

Nathan Eagle and Alex (Sandy) Pentland. 2006. Reality mining: Sensing complex social systems. Personal Ubiq. Comput. 10, 4 (Mar. 2006), 255--268.

Digital Library

[55]

Imme Ebert-Uphoff and Yi Deng. 2012. Causal discovery for climate research using graphical models. J. Climate 25, 17 (Feb. 2012), 5648--5665.

[56]

Bradley Efron and R. J. Tibshirani. 1993. An Introduction to the Bootstrap. Chapman 8 Hall.

[57]

Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J. Collins, and Timothy S. Gardner. 2007. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5, 1 (Jan. 2007), e8.

[58]

Damien R. Farine. 2017. A guide to null models for animal social network analysis. Methods Ecol. Evol. 8, 10 (2017), 1309--1320.

[59]

Damien R. Farine, Ariana Strandburg-Peshkin, Tanya Y. Berger-Wolf, Brian Ziebart, Ivan Brugere, Jia Li, and Margaret C. Crofoot. 2016. Both nearest neighbours and long-term affiliates predict individual locations during collective movement in wild baboons, In Nature Scientific Reports. Sci. Rep. 6, 27704.

[60]

Damien R. Farine and Hal Whitehead. 2015. Constructing, conducting and interpreting animal social network analysis. J. Animal Ecol. 84, 5 (2015), 1144--1163.

[61]

Soheil Feizi, Daniel Marbach, Muriel Medard, and Manolis Kellis. 2013. Network deconvolution as a general method to distinguish direct dependencies in networks. Nature Biotech. 31, 8 (Aug. 2013), 726--733.

[62]

Benjamin Fish and Rajmonda S. Caceres. 2017. A task-driven approach to timescale detection in dynamic networks. In Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG’17). http://www.mlgworkshop.org/2017/paper/MLG2017_paper_17.pdf.

[63]

Robert J. Fletcher, Miguel A. Acevedo, Brian E. Reichert, Kyle E. Pias, and Wiley M. Kitchens. 2011. Social network models predict movement and connectivity in ecological landscapes. Proc. Natl. Acad. Sci. U.S.A. 108, 48 (2011), 19282--19287.

[64]

Karl J. Friston, Baojuan Li, Jean Daunizeau, and Klaas E. Stephan. 2011. Network discovery with DCM. NeuroImage 56, 3 (Jun. 2011), 1202--1221.

[65]

Lise Getoor, Nir Friedman, Daphne Koller, Avi Pfeffer, and Ben Taskar. 2007. Probabilistic relational models. Introduction to Statistical Relational Learning (2007), 129. https://mitpress.mit.edu/books/introduction-statistical-relational-learning.

[66]

Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, and Edoardo M. Airoldi. 2010. A survey of statistical network models. Found. Trends Mach. Learn. 2, 2 (Feb. 2010), 129--233.

Digital Library

[67]

Manuel Gomez-Rodriguez, Jure Leskovec, David Balduzzi, and Bernhard Schölkopf. 2014. Uncovering the structure and temporal dynamics of information propagation. Network Sci. 2, 1, 26--65.

[68]

Manuel Gomez-Rodriguez, Jure Leskovec, and Andreas Krause. 2012. Inferring networks of diffusion and influence. ACM Trans. Knowl. Discov. Data 5, 4 (Feb. 2012), 21:1--21:37.

Digital Library

[69]

Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine (Runting) Shi, and Dawn Song. 2014. Joint link prediction and attribute inference using a social-attribute network. ACM Trans. Intell. Syst. Technol. 5, 2 (Apr. 2014), 27:1--27:20.

Digital Library

[70]

Palash Goyal and Emilio Ferrara. 2017. Graph embedding techniques, applications, and performance: A survey. CoRR abs/1705.02801 (2017). http://arxiv.org/abs/1705.02801

[71]

Clive W. J. Granger. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 3 (1969), 424--438. 00129682, 14680262

[72]

Michael D. Greicius, Ben Krasnow, Allan L. Reiss, and Vinod Menon. 2003. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U.S.A. 100, 1 (2003), 253--258.

[73]

Chris Groendyke, David Welch, and David R. Hunter. 2011. Bayesian inference for contact networks given epidemic data. Scand. J. Stat. 38, 3 (Sept. 2011), 600--616.

[74]

Huan Gui, Ya Xu, Anmol Bhasin, and Jiawei Han. 2015. Network A/B testing: From sampling to estimation. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). ACM, 399--409.

Digital Library

[75]

Hamed Haddadi, Andrew J. King, Alison P. Wills, Damien Fay, John Lowe, A. Jennifer Morton, Stephen Hailes, and Alan M. Wilson. 2011. Determining association networks in social animals: Choosing spatial-temporal criteria and sampling rates. Behav. Ecol. Sociobiol. 65, 8 (2011), 1659--1668.

[76]

Hamed Haddadi, Miguel Rio, Gianluca Iannaccone, Andrew Moore, and Richard Mortier. 2008. Network topologies: Inference, modeling, and generation. IEEE Commun. Surveys Tutor. 10, 2 (2008), 48--69.

Digital Library

[77]

Rodrigo K. Hamede, Jim Bashford, Hamish McCallum, and Menna Jones. 2009. Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: Using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease. Ecol. Lett. 12, 11 (2009), 1147--1157.

[78]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. ArXiv e-prints (Sept. 2017). https://arxiv.org/abs/1709.05584

[79]

Mohammad Al Hasan and Mohammed J. Zaki. 2011. A survey of link prediction in social networks. In Social Network Data Analytics SE - 9, Charu C. Aggarwal (Ed.). Springer US, 243--275.

[80]

Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona, and Jean-Philippe Vert. 2012. TIGRESS: Trustful inference of gene regulation using stability selection. BMC Syst. Biol. 6, 1 (2012), 145.

[81]

S. Havlin, D. Y. Kenett, E. Ben-Jacob, A. Bunde, R. Cohen, H. Hermann, J. W. Kantelhardt, J. Kertész, S. Kirkpatrick, J. Kurths, J. Portugali, and S. Solomon. 2012. Challenges in network science: Applications to infrastructures, climate, social systems and economics. Eur. Phys. J. Special Topics 214, 1 (2012), 273--293.

[82]

D. T. Haydon, M. Chase-Topping, D. J. Shaw, L. Matthews, J. K. Friar, J. Wilesmith, and M. E. J. Woolhouse. 2003. The construction and analysis of epidemic trees with reference to the 2001 UK foot-and-mouth outbreak. Proc. Roy. Soc. London B: Biol. Sci. 270, 1511 (Jan. 2003), 121--127.

[83]

Michael Hecker, Sandro Lambeck, Susanne Toepfer, Eugene van Someren, and Reinhard Guthke. 2009. Gene regulatory network inference: Data integration in dynamic models--A review. Biosystems 96, 1 (2009), 86--103.

[84]

Anthony Hey, Stewart Tansley, and Kristin Tolle. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research.

[85]

Jaroslav Hlinka, David Hartman, Martin Vejmelka, Jakob Runge, Norbert Marwan, Jürgen Kurths, and Milan Paluš. 2013. Reliability of inference of directed climate networks using conditional mutual information. Entropy 15, 6 (2013), 2023.

[86]

Jake M. Hofman and Chris H. Wiggins. 2008. Bayesian approach to network modularity. Phys. Rev. Lett. 100, 25 (Jun. 2008), 258701.

[87]

Petter Holme. 2015. Modern temporal network theory: A colloquium. Eur. Phys. J. B. 88, 9 (2015), 1--30.

[88]

Petter Holme and Jari Saramäki. 2012. Temporal networks. Phys. Rep. 519, 3 (2012), 97--125.

[89]

R. Matthew Hutchison, Thilo Womelsdorf, Elena A. Allen, Peter A. Bandettini, Vince D. Calhoun, Maurizio Corbetta, Stefania Della Penna, Jeff H. Duyn, Gary H. Glover, Javier Gonzalez-Castillo, Daniel A. Handwerker, Shella Keilholz, Vesa Kiviniemi, David A. Leopold, Francesco de Pasquale, Olaf Sporns, Martin Walter, and Catie Chang. 2013. Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage 80 (Oct. 2013), 360--378.

[90]

Alon Itai and Michael Rodeh. 1978. Finding a minimum circuit in a graph. SIAM J. Comput. 7, 4 (Nov. 1978), 413--423.

[91]

Richard James, Darren P. Croft, and Jens Krause. 2009. Potential banana skins in animal social network analysis. Behav. Ecol. Sociobiol. 63, 7 (2009), 989--997.

[92]

Jaya Kawale, Snigdhansu Chatterjee, Dominick Ormsby, Karsten Steinhaeuser, Stefan Liess, and Vipin Kumar. 2012. Testing the significance of spatio-temporal teleconnection patterns. In Proceedings of ACM SIGKDD 2012 International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM, 642--650.

Digital Library

[93]

Jaya Kawale, Stefan Liess, Arjun Kumar, Michael Steinbach, Peter Snyder, Vipin Kumar, Auroop R. Ganguly, Nagiza F. Samatova, and Fredrick Semazzi. 2013. A graph-based approach to find teleconnections in climate data. Stat. Anal. Data Min. 6, 3 (2013), 158--179.

Digital Library

[94]

Roland Kays, Margaret C. Crofoot, Walter Jetz, and Martin Wikelski. 2015. Terrestrial animal tracking as an eye on life and planet. Science 348, 6240 (2015).

[95]

Matt J. Keeling and Ken T. D. Eames. 2005. Networks and epidemic models. J. Roy. Soc. Interface 2, 4 (Sept. 2005), 295--307.

[96]

David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of ACM SIGKDD 2003 International Conference on Knowledge Discovery and Data Mining (KDD’03). ACM, 137--146.

Digital Library

[97]

Angelika Kimmig, Lilyana Mihalkova, and Lise Getoor. 2014. Lifted graphical models: A survey. Mach. Learn. 99, 1 (2014), 1--45.

Digital Library

[98]

Mikko Kivelä, Alex Arenas, Marc Barthelemy, James P. Gleeson, Yamir Moreno, and Mason A. Porter. 2014. Multilayer networks. J. Complex Networks 2, 3 (2014), 203--271.

[99]

Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, and Michael I. Jordan. 2014. A scalable bootstrap for massive data. J. Roy. Stat. Soc.: Series B (Statistical Methodology) 76, 4 (2014), 795--816.

[100]

Florian Klimm, Danielle S. Bassett, Jean M. Carlson, and Peter J. Mucha. 2014. Resolving structural variability in network models and the brain. PLoS Comput. Biol. 10, 3 (Mar. 2014), e1003491.

[101]

Bryan Klimt and Yiming Yang. 2004. The enron corpus: A new dataset for email classification research. In Proceedings of the Conference on Machine Learning (ECML’04). Springer, 217--226.

Digital Library

[102]

Ron Kohavi, Alex Deng, Brian Frasca, Toby Walker, Ya Xu, and Nils Pohlmann. 2013. Online controlled experiments at large scale. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). ACM, New York, NY, 1168--1176.

Digital Library

[103]

Eric D. Kolaczyk. 2009. Network topology inference. In Statistical Analysis of Network Data SE-7. Springer, New York, 1--48.

[104]

Mark A. Kramer, Uri T. Eden, Sydney S. Cash, and Eric D. Kolaczyk. 2009. Network inference with confidence from multivariate time series. Phys. Rev. E. 79, 6 (Jun. 2009), 61916.

[105]

Mark A. Kramer, Eric D. Kolaczyk, and Heidi E. Kirsch. 2008. Emergent network topology at seizure onset in humans. Epilepsy Res. 79, 2--3 (2008), 173--186.

[106]

Jens Krause, Stefan Krause, Robert Arlinghaus, Ioannis Psorakis, Stephen Roberts, and Christian Rutz. 2013. Reality mining of animal social systems. Trends Ecol. Evol. 28, 9 (Sept. 2013), 541--551.

[107]

Marlene Kretschmer, Dim Coumou, Jonathan F. Donges, and Jakob Runge. 2016. Using causal effect networks to analyze different arctic drivers of mid-latitude winter circulation. J. Climate (Mar 2016).

[108]

Timothy La Fond and Jennifer Neville. 2010. Randomization tests for distinguishing social influence and homophily effects. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, 601--610.

Digital Library

[109]

Jean-Philippe Lachaux, Antoine Lutz, David Rudrauf, Diego Cosmelli, Michel Le Van Quyen, Jacques Martinerie, and Francisco Varela. 2002. Estimating the time-course of coherence between single-trial brain signals: An introduction to wavelet coherence. Neurophys. Clin./Clin. Neurophysiol. 32, 3 (Jun. 2002), 157--174.

[110]

N. D. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, and A. T. Campbell. 2010. A survey of mobile phone sensing. IEEE Commun. Mag. 48, 9 (Sept. 2010), 140--150.

Digital Library

[111]

Juha K. Laurila, Daniel Gatica-Perez, Imad Aad, Jan Blom, Olivier Bornet, Trinh Minh Tri Do, Olivier Dousse, Julien Eberle, and Markus Miettinen. 2013. From big smartphone data to worldwide research: The mobile data challenge. Pervas. Mobile Comput. 9, 6 (Dec. 2013), 752--771.

Digital Library

[112]

David Lazer. 2011. Networks in political science: Back to the future. Polit. Sci. Pol. 44, 01 (2011), 61--68.

[113]

Sophie Lebre, Jennifer Becq, Frederic Devaux, Michael Stumpf, and Gaelle Lelandais. 2010. Statistical inference of the time-varying structure of gene-regulation networks. BMC Syst. Biol. 4, 1 (2010), 130.

[114]

Jure Leskovec and Eric Horvitz. 2014. Geospatial structure of a planetary-scale social network. IEEE Trans. Comput. Social Syst. 1, 3 (Sept. 2014), 156--163.

[115]

S. Levitus, J. I. Antonov, O. K. Baranova, T. P. Boyer, C. L. Coleman, H. E. Garcia, A. I. Grodsky, D. R. Johnson, R. A. Locarnini, A. V. Mishonov, J. R. Reagan, C. L. Sazama, D. Seidov, I. Smolyar, E. S. Yarosh, and M. M. Zweng. 2013. The world ocean database. Data Sci. J. 12 (2013), WDS229--WDS234.

[116]

Huai Li, Jianhua Xuan, Yue Wang, and Ming Zhan. 2008. Inferring regulatory networks. Front. Biosci. 13 (2008), 263--275.

[117]

David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. J. Amer. Soc. Info. Sci. Technol. 58, 7 (May 2007), 1019--1031.

Digital Library

[118]

Linyuan Lü and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A: Stat. Mech. Appl. 390, 6 (2011), 1150--1170.

[119]

Subramani Mani and Gregory F. Cooper. 2004. Causal discovery using a bayesian local causal discovery algorithm. Studies Health Technol. Info. 107, Pt 1 (2004), 731--735.

[120]

Daniel Marbach, James C. Costello, Robert Kuffner, Nicole M. Vega, Robert J. Prill, Diogo M. Camacho, Kyle R. Allison, Manolis Kellis, James J. Collins, and Gustavo Stolovitzky. 2012. Wisdom of crowds for robust gene network inference. Nature Methods 9, 8 (Aug. 2012), 796--804.

[121]

Rossana Mastrandrea, Julie Fournet, and Alain Barrat. 2015. Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS ONE 10, 9 (Sept. 2015), e0136497.

[122]

Julian McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring networks of substitutable and complementary products. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’15). ACM, 785--794.

Digital Library

[123]

David B. McDonald. 2007. Predicting fate from early connectivity in a social network. Proc. Natl. Acad. Sci. U.S.A. 104, 26 (Jun. 2007), 10910--10914.

[124]

Christopher Meek. 1995. Causal inference and causal explanation with background knowledge. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI’95). Morgan Kaufmann Publishers Inc., 403--410. http://dl.acm.org/citation.cfm?id=2074204

Digital Library

[125]

Patrick Meyer, Kevin Kontos, Frederic Lafitte, and Gianluca Bontempi. 2007. Information-theoretic inference of large transcriptional regulatory networks. EURASIP J. Bioinfo. Syst. Biol. 2007, 1 (2007), 79879.

Digital Library

[126]

Lauren Ancel Meyers, Babak Pourbohloul, M. E. J. Newman, Danuta M. Skowronski, and Robert C. Brunham. 2005. Network theory and SARS: Predicting outbreak diversity. J. Theoret. Biol. 232, 1 (2005), 71--81.

[127]

Isobel Milns, Colin M. Beale, and V. Anne Smith. 2010. Revealing ecological networks using Bayesian network inference algorithms. Ecology 91, 7 (Feb. 2010), 1892--1899.

[128]

Sach Mukherjee and Terence P. Speed. 2008. Network inference using informative priors. Proc. Natl. Acad. Sci. U.S.A. 105, 38 (Sept. 2008), 14313--14318.

[129]

Seth Myers and Jure Leskovec. 2010. On the convexity of latent social network inference. In Advances in Neural Information Processing Systems 23. 1741--1749. http://papers.nips.cc/paper/4113-on-the-convexity-of-latent-social-network-inference.

Digital Library

[130]

Galileo Mark Namata, Ben London, and Lise Getoor. 2015. Collective graph identification. ACM Transactions on Knowledge Discovery from Data.

Digital Library

[131]

G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. 1978. An analysis of approximations for maximizing submodular set functions--I. Math. Program. 14, 1 (1978), 265--294.

Digital Library

[132]

Praneeth Netrapalli and Sujay Sanghavi. 2012. Learning the graph of epidemic cascades. SIGMETRICS Perform. Eval. Rev. 40, 1 (Jun. 2012), 211--222.

Digital Library

[133]

Jian Ni, Haiyong Xie, Sekhar Tatikonda, and Yang Richard Yang. 2010. Efficient and dynamic routing topology inference from end-to-end measurements. IEEE/ACM Trans. Network. 18, 1 (Feb. 2010), 123--135.

Digital Library

[134]

M. Paluš, D. Hartman, J. Hlinka, and M. Vejmelka. 2011. Discerning connectivity from dynamics in climate networks. Nonlin. Process. Geophys. 18, 5 (2011), 751--763.

[135]

Evangelos E. Papalexakis, Alona Fyshe, Nicholas D. Sidiropoulos, Partha Pratim Talukdar, Tom M. Mitchell, and Christos Faloutsos. 2014. Good-enough brain model: Challenges, algorithms and discoveries in multi-subject experiments. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14). ACM, 95--104.

Digital Library

[136]

D. M. Patrick, M. L. Rekart, A. Jolly, S. Mak, M. Tyndall, J. Maginley, E. Wong, T. Wong, H. Jones, C. Montgomery, and R. C. Brunham. 2002. Heterosexual outbreak of infectious syphilis: Epidemiological and ethnographic analysis and implications for control. Sex. Transmit. Infect. 78, suppl 1 (2002), i164--i169.

[137]

Joseph J. Pfeiffer III, Sebastian Moreno, Timothy La Fond, Jennifer Neville, and Brian Gallagher. 2014. Attributed graph models: Modeling network structure with correlated attributes. In Proceedings of the 23rd International Conference on World Wide Web (WWW’14). ACM, 831--842.

Digital Library

[138]

Gert Pfurtscheller and Colin Andrew. 1999. Event-related changes of band power and coherence: Methodology and interpretation. J. Clin. Neurophysiol. 16, 6 (Nov. 1999), 512--519.

[139]

Noa Pinter-Wollman, Elizabeth A. Hobson, Jennifer E. Smith, Andrew J. Edelman, Daizaburo Shizuka, Shermin de Silva, James S. Waters, Steven D. Prager, Takao Sasaki, George Wittemyer, Jennifer Fewell, and David B. McDonald. 2013. The dynamics of animal social networks: Analytical, conceptual, and theoretical advances. Behavioral Ecology. Oxford University Press.

[140]

S. C. Ponten, F. Bartolomei, and C. J. Stam. 2016. Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures. Clin. Neurophysiol. 118, 4 (Apr. 2016), 918--927.

[141]

Stephen R. Proulx, Daniel E. L. Promislow, and Patrick C. Phillips. 2005. Network thinking in ecology and evolution. Trends Ecol. Evol. 20, 6 (2005), 345--353.

[142]

Nataša Pržulj, Derek G. Corneil, and Igor Jurisica. 2004. Modeling interactome: Scale-free or geometric? Bioinformatics 20, 18 (2004), 3508--3515.

[143]

Ioannis Psorakis, Stephen J. Roberts, Iead Rezek, and Ben C. Sheldon. 2012. Inferring social network structure in ecological systems from spatio-temporal data streams. J. Roy. Soc. Interface 9, 76 (2012), 3055--3066.

[144]

Manish Purohit, B. Aditya Prakash, Chanhyun Kang, Yao Zhang, and V. S. Subrahmanian. 2014. Fast influence-based coarsening for large networks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’14). ACM, 1296--1305.

Digital Library

[145]

J. D. Ramsey, S. J. Hanson, C. Hanson, Y. O. Halchenko, R. A. Poldrack, and C. Glymour. 2010. Six problems for causal inference from fMRI. NeuroImage 49, 2 (2010), 1545--1558.

[146]

Carlo Ratti, Stanislav Sobolevsky, Francesco Calabrese, Clio Andris, Jonathan Reades, Mauro Martino, Rob Claxton, and Steven H. Strogatz. 2010. Redrawing the map of great britain from a network of human interactions. PLoS ONE 5, 12 (Dec. 2010), e14248.

[147]

Jaap C. Reijneveld, Sophie C. Ponten, Henk W. Berendse, and Cornelis J. Stam. 2007. The application of graph theoretical analysis to complex networks in the brain. Clin. Neurophysiol. 118, 11 (Dec. 2007), 2317--2331.

[148]

Bruno Ribeiro, Nicola Perra, and Andrea Baronchelli. 2013. Quantifying the effect of temporal resolution on time-varying networks. Sci. Rep. 3 (Oct. 2013), 3006.

[149]

Lucy F. Robinson, Lauren Y. Atlas, and Tor D. Wager. 2015. Dynamic functional connectivity using state-based dynamic community structure: Method and application to opioid analgesia. NeuroImage 108 (2015), 274--291.

[150]

Alard Roebroeck, Elia Formisano, and Rainer Goebel. 2005. Mapping directed influence over the brain using granger causality and fMRI. NeuroImage 25, 1 (2005), 230--242.

[151]

M. J. Rosa, K. Friston, and W. Penny. 2012. Post-hoc selection of dynamic causal models. J. Neurosci. Methods 208, 1 (2012), 66--78.

[152]

Mikail Rubinov and Olaf Sporns. 2010. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 3 (Sept. 2010), 1059--1069.

[153]

Jakob Runge, Vladimir Petoukhov, and Jürgen Kurths. 2013. Quantifying the strength and delay of climatic interactions: The ambiguities of cross correlation and a novel measure based on graphical models. J. Climate 27, 2 (Sept. 2013), 720--739.

[154]

Christian Rutz, Zackory T. Burns, Richard James, Stefanie M. H. Ismar, John Burt, Brian Otis, Jayson Bowen, and James J. H. St Clair. 2012. Automated mapping of social networks in wild birds. Curr. Biol. 22, 17 (2012), R669--R671.

[155]

Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquías Peña Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker. 2014. The NCEP climate forecast system version 2. J. Climate 27, 6 (2014), 2185--2208.

[156]

Vangelis Sakkalis. 2011. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Comp. Biol. Med. 41, 12 (2011), 1110--1117.

Digital Library

[157]

Jari Saramäki and Esteban Moro. 2015. From seconds to months: An overview of multi-scale dynamics of mobile telephone calls. Eur. Phys. J. B. 88, 6 (2015), 1--10.

[158]

Vedran Sekara and Sune Lehmann. 2014. The strength of friendship ties in proximity sensor data. PLoS ONE 9, 7 (July 2014), e100915.

[159]

Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI Mag. 29, 3 (2008), 93. http://hdl.handle.net/1903/7546

Digital Library

[160]

Jay Shendure and Hanlee Ji. 2008. Next-generation DNA sequencing. Nature Biotechnol. 26, 10 (Oct. 2008), 1135--1145.

[161]

Caroline Siegenthaler and Rudiyanto Gunawan. 2014. Assessment of network inference methods: How to cope with an underdetermined problem. PLoS ONE 9, 3 (03 2014), 1--7.

[162]

Chao Sima, Jianping Hua, and Sungwon Jung. 2009. Inference of gene regulatory networks using time-series data: A survey. Curr. Genom. 10, 6 (Sept. 2009), 416--429.

[163]

Sean L. Simpson, F. DuBois Bowman, and Paul J. Laurienti. 2013. Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain. Stat. Surveys 7 (2013), 1--36.

[164]

Timo Smieszek, Stefanie Castell, Alain Barrat, Ciro Cattuto, Peter J. White, and Gérard Krause. 2016. Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: Method comparison and participants’ attitudes. BMC Infect. Dis. 16, 1 (2016), 1--14.

[165]

Orr Spiegel, Stephan T. Leu, Andrew Sih, and C. Michael Bull. 2016. Socially interacting or indifferent neighbours? Randomization of movement paths to tease apart social preference and spatial constraints. Methods Ecol. Evol. 7, 8 (2016), 971--979.

[166]

Olaf Sporns. 2014. Contributions and challenges for network models in cognitive neuroscience. Nature Neurosci. 17, 5 (May 2014), 652--660.

[167]

Olaf Sporns and Richard F. Betzel. 2016. Modular brain networks. Annu. Rev. Psychol. 67, 1 (Jan. 2016), 613--640.

[168]

J. Conrad Stack, Shweta Bansal, V. S. Anil Kumar, and Bryan Grenfell. 2013. Inferring population-level contact heterogeneity from common epidemic data. J. Roy. Soc. Interface 10, 78 (2013).

[169]

Juliette Stehlé, François Charbonnier, Tristan Picard, Ciro Cattuto, and Alain Barrat. 2013. Gender homophily from spatial behavior in a primary school: A sociometric study. Social Networks 35, 4 (2013), 604--613.

[170]

Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop R. Ganguly. 2011. Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat. Anal. Data Min. 4, 5 (2011), 497--511.

Digital Library

[171]

Arkadiusz Stopczynski, Riccardo Pietri, Alex Pentland, David Lazer, and Sune Lehmann. 2014. Privacy in sensor-driven human data collection: A guide for practitioners. ArXiv e-prints (Mar. 2014). https://arxiv.org/abs/1403.5299

[172]

Arkadiusz Stopczynski, Vedran Sekara, Piotr Sapiezynski, Andrea Cuttone, Mette My Madsen, Jakob Eg Larsen, and Sune Lehmann. 2014. Measuring large-scale social networks with high resolution. PLoS ONE 9, 4 (Apr. 2014), e95978.

[173]

Cédric Sueur, Armand Jacobs, Frédéric Amblard, Odile Petit, and Andrew J. King. 2011. How can social network analysis improve the study of primate behavior? Amer. J. Primatol. 73, 8 (Aug. 2011), 703--19.

[174]

Rajmonda Sulo, Tanya Berger-Wolf, and Robert Grossman. 2010. Meaningful selection of temporal resolution for dynamic networks. In Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG’10). ACM, New York, NY, 127--136.

Digital Library

[175]

Kaustubh Supekar, Vinod Menon, Daniel Rubin, Mark Musen, and Michael D. Greicius. 2008. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput. Biol. 4, 6 (2008), 1--11.

[176]

Kaustubh Supekar, Vinod Menon, Daniel Rubin, Mark Musen, and Michael D. Greicius. 2008. Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput. Biol. 4, 6 (Jun. 2008), e1000100.

[177]

Yuriy Sverchkov and Mark Craven. 2017. A review of active learning approaches to experimental design for uncovering biological networks. PLOS Comput. Biol. 13, 6 (2017), 1--26.

[178]

Hiroyuki Toh and Katsuhisa Horimoto. 2002. Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics 18, 2 (2002), 287--297.

[179]

Anastasios A. Tsonis and Paul J. Roebber. 2004. The architecture of the climate network. Physica A: Stat. Mech. Appl. 333, 0 (2004), 497--504.

[180]

Anastasios A. Tsonis, Geli Wang, Kyle L. Swanson, Francisco A. Rodrigues, and Luciano da Fontura Costa. 2011. Community structure and dynamics in climate networks. Climate Dynam. 37, 5--6 (2011), 933--940.

[181]

Charalampos E. Tsourakakis, U. Kang, Gary L. Miller, and Christos Faloutsos. 2009. DOULION: Counting triangles in massive graphs with a coin. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). ACM, 837--846.

Digital Library

[182]

Liubov Tupikina, Nora Molkenthin, Cristóbal López, Emilio Hernández-García, Norbert Marwan, and Jürgen Kurths. 2016. Correlation networks from flows. The case of forced and time-dependent advection-diffusion dynamics. PLoS ONE 11, 4 (04 2016), 1--12.

[183]

Toni Vallès-Català, Tiago P. Peixoto, Roger Guimerà, and Marta Sales-Pardo. 2017. On the consistency between model selection and link prediction in networks. ArXiv e-prints (May 2017). https://arxiv.org/abs/1705.07967

[184]

Martijn P. van den Heuvel, René S. Kahn, Joaquín Goñi, and Olaf Sporns. 2012. High-cost, high-capacity backbone for global brain communication. Proc. Natl. Acad. Sci. U.S.A. 109, 28 (July 2012), 11372--11377.

[185]

Martijn P. van den Heuvel and Olaf Sporns. 2011. Rich-club organization of the human connectome. J. Neurosci. 31, 44 (Nov. 2011), 15775--15786.

[186]

Martijn P. van den Heuvel and Olaf Sporns. 2015. Network hubs in the human brain. Trends Cogn. Sci. 17, 12 (Dec. 2015), 683--696.

[187]

Dan J. Wang, Xiaolin Shi, Daniel A. McFarland, and Jure Leskovec. 2012. Measurement error in network data: A re-classification. Soc. Networks 34, 4 (2012), 396--409.

[188]

Larry Wasserman. 2006. All of Nonparametric Statistics (Springer Texts in Statistics). Springer, New York.

Digital Library

[189]

Duncan J. Watts and Steven H. Strogatz. 1998. Collective dynamics of “small-world” networks. Nature 393, 6684 (Jun. 1998), 440--442.

[190]

David Welch, Shweta Bansal, and David R. Hunter. 2011. Statistical inference to advance network models in epidemiology. Epidemics 3, 1 (Mar. 2011), 38--45.

[191]

Tina Wey, Daniel T. Blumstein, Weiwei Shen, and Ferenc Jordán. 2008. Social network analysis of animal behaviour: A promising tool for the study of sociality. Animal Behav. 75, 2 (2008), 333--344.

[192]

Hal Whitehead, Lars Bejder, and C. Andrea Ottensmeyer. 2005. Testing association patterns: Issues arising and extensions. Animal Behav. 69, 5 (2005).

[193]

Hal Whitehead and Richard James. 2015. Generalized affiliation indices extract affiliations from social network data. Methods Ecol. Evol. 6, 7 (2015), 836--844.

[194]

R. W. Wilkins, D. A. Hodges, P. J. Laurienti, M. Steen, and J. H. Burdette. 2014. Network science and the effects of music preference on functional brain connectivity: From beethoven to eminem. Sci. Rep. 4 (Aug. 2014), 6130.

[195]

Rongjing Xiang and Jennifer Neville. 2008. Pseudolikelihood EM for within-network relational learning. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). 1103--1108.

Digital Library

[196]

K. Yamasaki, A. Gozolchiani, and S. Havlin. 2008. Climate networks around the globe are significantly affected by El Niño. Phys. Rev. Lett. 100, 22 (Jun. 2008), 228501.

[197]

Donghyeon Yu, MinSoo Kim, Guanghua Xiao, and Tae Hyun Hwang. 2013. Review of biological network data and its applications. Genom. Informat. 11, 4 (Dec. 2013), 200--210.

[198]

Qingbao Yu, Erik B. Erhardt, Jing Sui, Yuhui Du, Hao He, Devon Hjelm, Mustafa S. Cetin, Srinivas Rachakonda, Robyn L. Miller, Godfrey Pearlson, and Vince D. Calhoun. 2015. Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. NeuroImage 107 (2015), 345--355.

[199]

Ming Yuan and Yi Lin. 2006. Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc: Series B (Stat. Methodol.) 68, 1 (Feb. 2006), 49--67.

[200]

Andrew Zalesky, Alex Fornito, and Ed Bullmore. 2012. On the use of correlation as a measure of network connectivity. NeuroImage 60, 4 (2012), 2096--2106.

[201]

Andrew Zalesky, Alex Fornito, Ian H. Harding, Luca Cocchi, Murat Yücel, Christos Pantelis, and Edward T. Bullmore. 2010. Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage 50, 3 (Apr. 2010), 970--983.

[202]

Tanja Zerenner, Petra Friederichs, Klaus Lehnertz, and Andreas Hense. 2014. A gaussian graphical model approach to climate networks. Chaos 24, 2 (2014).

[203]

Yang Zhan, David Halliday, Ping Jiang, Xuguang Liu, and Jianfeng Feng. 2006. Detecting time-dependent coherence between non-stationary electrophysiological signals--A combined statistical and time-frequency approach. J. Neurosci. Methods 156, 1--2 (Sept. 2006), 322--332.

[204]

Bin Zhang and Steve Horvath. 2005. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, 1 (2005).

[205]

Dong Zhou, Avi Gozolchiani, Yosef Ashkenazy, and Shlomo Havlin. 2015. Teleconnection paths via climate network direct link detection. Phys. Rev. Lett. 115, 26 (Dec. 2015), 268501.

[206]

Hui Zhou, Wencai Du, Shaochun Xu, and Qinling Xin. 2011. An empirical study of network topology inference. In Computer and Information Science 2011 SE-17, Roger Lee (Ed.). Studies in Computational Intelligence, Vol. 364. Springer, Berlin, 213--225.

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Index Terms

Network Structure Inference, A Survey: Motivations, Methods, and Applications
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
    2. Machine learning approaches
      1. Learning in probabilistic graphical models
  2. Modeling and simulation
    1. Simulation theory
      1. Network science
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Temporal data
      2. Graph-based database models
        Network data models
  2. Information systems applications
    1. Data mining

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 2

March 2019

748 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3186333

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Published: 17 April 2018

Accepted: 01 October 2017

Revised: 01 August 2017

Received: 01 October 2016

Published in CSUR Volume 51, Issue 2

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