Network Science
Tutorials

General Network
Network Topology
Static Network Estimate
Dynamic Network Estimate

Community structure Detection

Specific Types of Networks
Biological Networks

Brain's Network of Neurons

Cognitive Networks

Communication Networks

Cyber-physical System

Economic Networks

Power Grid
Social Networks
Transportation Networks
 

Curriculum for Training Network Science Researcher

 

Useful links

 

 

Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles, algorithms and tools that govern network behavior.

The National Research Council defines Network Science as "the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena." (wikipedia)

Tutorials

bullet National Research Council. Network Science. National Academies Press, 2005.
bullet Mark E. J. Newman, Albert-László Barabási, Duncan J. Watts. The Structure and Dynamics of Networks. Princeton University Press, 2006.
bullet François Képčs, Francois Kepes. Biological networks. World Scientific, 2007.
bullet Björn H. Junker, Falk Schreiber. Analysis of biological networks. Wiley-Interscience, 2008.
bullet Ted G. Lewis. Network Science: Theory and Applications. John Wiley and Sons, 2009.

General Network Problems

Network Topology Modeling

bullet William Aiello, Fan Chung, Linyuan Lu. A random graph model for massive graphs. Annual ACM Symposium on Theory of Computing. Portland, Oregon, United States. p171 - 180. 2000.
bullet Jon Michael Kleinberg. The small-world phenomenon: an algorithm perspective. Annual ACM Symposium on Theory of Computing, Proceedings of the thirty-second annual ACM symposium on Theory of computing. Portland, Oregon, United States. p163 - 170. 2000.
bullet William Aiello, Fan Chung. Random Evolution in Massive Graphs. FOCS, Proceedings of the 42nd IEEE symposium on Foundations of Computer Science. p510. 2001.
bullet William Aiello, Fan Chung, and Linyuan Lu. A random graph model for power law graphs. Experiment. Math. Volume 10, Issue 1 (2001), 53-66.
bullet Ruomei Gao, Ellen Zegura. An Evolutionary Framework for AS-level Internet Topology Modeling. Globecom-New York. 2003, Vol 7, p. 3824-3829.
bullet Fan Chung and Linyuan Lu. Coupling online and offline analysis for random power law graphs. Internet Math. Volume 1, Number 4 (2003), 409-461.
bullet Nima Sarshar and Vwani Roychowdhury. Scale-free and Stable Structures in Complex ad hoc Networks. Physical Review E 69, 026101 (2004).
bullet Ronen Olinky and Lewi Stone. Unexpected Epidemic Thresholds in Heterogeneous Networks: The Role of Disease Transmission. Physical Review E 70, 030902(R) (2004).
bullet Tao Zhou, Jian-Guo Liu, Wen-Jie Bai, Guanrong Chen, and bing-Hong Wang. Behaviors of Susceptible-infected Epidemics on Scale-free Networks with Identical Infectivity. Physical Review E 74, 056109 (2006).
bullet Danuta Makowiec. From Regular lattice to Scale Free Network- Yet Another Algorithm. Phys Rev E.2008.
bullet Srinivas Shakkottai, Marina Fomenkov, Ryan Koga, Dmitri Krioukov, and Kc Claffy. Evolution of the Internet As-Level Ecosystem. Springer Berlin Heidelberg, 2009, p. 1605.
bullet Yubo Wang, Gaoxi Xiao, Jie Hu, Tee Hiang Cheng, Limsoon Wang. Imperfect Targeted Immunization in Scale-free Networks. Physica A 388 (2009) 2535-2546.
bullet Svante Janson Tomasz Luczak and Ilkka Norros. Large cliques in a power-law random graph. Submitted. 2009.

Static Network Estimate

bullet N. Meinshausen and P. Buhlmann. High-dimensional Graphs and Variables Selection with the Lasso. Annals of Statistics, 34:1436, 2006.
bullet J. Friedman, T. Hastie, and R. Tibshirani. Sparse Inverse Covariance Estimation with the Graphical Lasso. Biostat, page kxm045, 2007b.
bullet Duchi, S. Gould, and D. Koller. Projected Subgradient Methods for Learning Sparse Gaussians. In Proceedings of the Twenty-fourth Conference on Uncertainty in AI (UAI), 2008a.
bullet P. Ravikumar, M.J. Wainwright, G. Rashutti, and B.Yu, High-dimensional Covariance Estimation by Minimizing l1-penalized log-determinant Divergence. Nov.2008.
bullet Alessandro Ferrante and Gopal Pandurangan and Kihong Park. On the Hardness of Optimization in Power Law source. Theoretical Computer Science 393 (2008) 220-230.
bullet J. Fan, Y. Feng, and Y. Wu. Network exploration via the adaptive lasso and scad penalties. Annals of Applied Statistics. Submitted.

Time-varying Network Estimate

bullet N. Luscombe, M. Babu, H. Yu, M. Snyder, S. Teichmann, and M. Gerstein. Genomic Analysis of Regulatory Network Dynamics Reveals Large Topological Changes. Nature, 431:308-312, 2004.
bullet S. Hannel and E. P. Xing. Discrete Temporal Models of Social Networks. Workshop on Statistical Network Analysis, the 23rd International Conference on machine Learning, 2006.
bullet Guo, S. Hanneke, W. Fu, and E.P.Xing. Recovering Temporally Rewiring Networks: A Model-based Approach. International Conference of Machine Learning, 2007.
bullet O. Banerjee, L. El Ghaoui, and A. d'Aspremont. Model Selection Through Sparse Maximum Likelihood Estimation. J. Mach. Learn. Res., 9:485-516, 2008.
bullet J. Peng, P. Wang, N. Zhou, and J. Zhu. Partial Correlation Estimation by Joint Sparse Regression models. 2008.
bullet M. Kolar, L. Song, A. Ahmed, and E. P. Xing, Estimating Time-Varying Networks , Annals of Applied Statistics, 2009. (earlier version appeared in arXiv:0812.5087)
bullet M. Kolar, L. Song and E. P. Xing, Sparsistent Learning of Varying-coefficient Models with Structural Changes, Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009).
bullet L. Song, M. Kolar and E. P. Xing, Time-Varying Dynamic Bayesian Networks, Proceeding of the 23rd Neural Information Processing Systems, (NIPS 2009).
bullet E.P. Xing, W. Fu, and L. Song, A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography, Annals of Applied Statistics, 2009. (earlier version appeared in arXiv:0901.0135)

Community structure Detection

bullet L. Donetti and M. A. Muńoz, Detecting Network Communities: a new systematic and powerful algorithm,  J. Stat. Mech.: Theor. Exp. 2004 [Spectral]
bullet M. J. Newman, Modularity and community structure in networks, PNAS, 2006 [Spectral]
bullet Martin Rosvall and Carl T. Bergstrom, An information-theoretic framework for resolving community structure in complex networks, PNAS, 2006 [MDL]
bullet Martin Rosvall and Carl T. Bergstrom, Maps of random walks on complex networks reveal community structure, PNAS, 2008 [InfoMap]
bullet J.-P. Onnela, J. Saramäki , J. Kertész, K. Kaski, Intensity and coherence of motifs in weighted complex networks, Phys. Rev. E 71, 2005 [CPMw]
bullet Peter Ronhovde and Zohar Nussinov, Local resolution-limit-free Potts model for community detection, Phys. Rev. E 81, 046114, 2010 [Pott Model]

Specific Types of Networks

Biological Networks

bullet Kuang-Chi Chen, Tse-Yi Wang, Huei-Hun Tseng, Chi-Ying F. Huang, and Cheng-Yan Kao. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics, Volume 21, Number 12, 15 June 2005. p2883-2890.
bullet Chang YH, Wang YC, Chen BS. Identification of transcription factor cooperativity via stochastic system model. Bioinformatics 2006 , 22(18):2276-2282.
bullet Adriana Climescu-Haulica and Michelle D Quirk. A stochastic differential equation model for transcriptional regulatory networks. BMC Bioinformatics. 2007; 8(Suppl 5): S4.
bullet Rui-Sheng Wang, Xiang-Sun Zhang and Luonan Chen. Inferring Transcriptional Interactions and regulator Activities from Experimental Data. Molecules and Cells. 31 December 2007 Volume 24, Number 3, pp. 307-315.
bullet

Kevin Y. Yip, Roger P. Alexander, Koon-Kiu K. Yan, Mark Gerstein. Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data.PloS one, Vol. 5, No. 1. (26 January 2010), e8121.

 

Brain's Network

bullet Valdés-Sosa PA, Sánchez-Bornot JM, Lage-Castellanos A, Vega-Hernández M. Estimating brain functional connectivity with sparse multivariate autoregression. Phil. Trans. R. Soc. B(2005). 360, 969-981.

Cognitive Networks

 

Communication Networks

bullet Christos Gkantsidis and Milena Mihail and Amin Saberi. Conductance and Congestion in Power Law Graphs. Joint International Conference on Measurement and Modeling of Computer Systems, Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems. San Diego, CA, USA. SESSION: Internet characterization. p148 - 159 . 2003.
bullet http://en.wikipedia.org/wiki/Communication_networks

Cyber-physical System

bullet http://en.wikipedia.org/wiki/Cyber-physical_system

Economic Networks

bullet http://en.wikipedia.org/wiki/Stock_market

Power Grid

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MARIJA D. ILIC′ , ERIC H. ALLEN, JEFFREY W. CHAPMAN, CHARLES A. KING, JEFFREY H. LANG, AND EUGENE LITVINOV. Preventing Future Blackouts by Means of Enhanced Electric Power Systems Control: From Complexity to Order. PROCEEDINGS OF THE IEEE, VOL. 93, NO. 11, NOVEMBER 2005. 1920-1941.

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Marija D. Ilic. From Hierarchical to Open Access Electric Power Systems. Proceedings of the IEEE, 95(5) May 2007,1060-1084.

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List of papers on Cascading Failure on Power grids.

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List of papers on Cascading Failure on Complex networks.

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Books on Power grid:

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Allen J. Wood, Bruce F. Wollenberg. Power Generation, Operation, and Control. Wiley, Jan 1996.

bullet

Arthur R. Bergen, Vijay Vittal. Power Systems Analysis. Pearson, 2000.

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By J. Duncan Glover, Mulukutla S. Sarma, Thomas J. Overbye. Power Systems Analysis and Design. Cengage Learning, 2008.

Social Networks

bullet David Kempe, Jon Kleinberg, Éva Tardos. Maximizing the Spread of Influence through a Social Network. Kempe, Jon Kleinberg, Éva Tardos. KDD 2003. p137--146.
bullet Stephen Eubank. V.S. Anil Kumar. Madhav V. Marathe. Aravind Srinivasany. Nan Wangz. Structural and algorithmic aspect of massive social network. Symposium on Discrete Algorithms. Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms. New Orleans, Louisiana. p718 - 727. 2004.
bullet David Kempe and Jon Kleinberg and Éva Tardos, Influential Nodes in a Diffusion Model for Social Networks. Proc. 32nd International Colloquium on Automata, Languages and Programming (ICALP), p1127--1138.
bullet Elchanan Mossel, Sebastien Roch. On the submodularity of influence in social networks. Annual ACM Symposium on Theory of Computing, Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. San Diego, California, USA. SESSION: Session 3B. p128 - 134. 2007.
bullet Ning Chen. On the approximability of influence in social networks. SIAM Journal on Discrete Mathematics archive. Volume 23 , Issue 3 (July 2009).
bullet http://en.wikipedia.org/wiki/Social_networks
bullet http://sspnet.eu/

Transportation Networks

bullet http://en.wikipedia.org/wiki/Transportation_network_(graph_theory).

Curriculum for Training Network Science Researcher

click here.

Useful links

bullet Complex Network's Blog.

 

biological networks


social networks


economic networks


power grid

communication networks

transportation networks

Contributed by: Dr. Dapeng Wu, Yuejia He, Zongrui Ding, Huanghuang Li, Jangping Wang.

last update: 2010-10-31