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
Cyberphysical 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
General Network
Problems
Network Topology Modeling

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. 

Jon Michael Kleinberg.
The smallworld
phenomenon: an algorithm perspective.
Annual ACM Symposium on Theory of
Computing, Proceedings of the thirtysecond annual ACM symposium on Theory
of computing. Portland,
Oregon, United States. p163  170. 2000. 

William Aiello,
Fan Chung.
Random Evolution in Massive Graphs.
FOCS, Proceedings of the 42nd IEEE
symposium on Foundations of Computer Science. p510. 2001. 

William Aiello, Fan Chung, and Linyuan Lu.
A random graph model for power law graphs.
Experiment. Math.
Volume 10, Issue 1 (2001), 5366. 

Ruomei Gao, Ellen Zegura.
An Evolutionary Framework for ASlevel Internet Topology Modeling.
GlobecomNew York. 2003, Vol 7, p. 38243829. 

Fan Chung and Linyuan Lu.
Coupling online and offline analysis for random power law graphs.
Internet Math. Volume 1, Number 4 (2003), 409461. 

Nima Sarshar and Vwani Roychowdhury.
Scalefree and Stable Structures in Complex ad hoc Networks.
Physical Review E 69, 026101 (2004). 

Ronen Olinky and Lewi Stone. Unexpected
Epidemic
Thresholds in Heterogeneous Networks: The Role of Disease Transmission.
Physical Review E 70, 030902(R) (2004). 

Tao Zhou, JianGuo Liu, WenJie Bai, Guanrong Chen, and bingHong Wang.
Behaviors of Susceptibleinfected Epidemics on Scalefree Networks with
Identical Infectivity. Physical Review E 74, 056109 (2006). 

Danuta Makowiec.
From
Regular lattice to Scale Free Network Yet Another Algorithm. Phys
Rev E.2008. 

Srinivas Shakkottai, Marina Fomenkov, Ryan
Koga, Dmitri Krioukov, and Kc Claffy.
Evolution
of the Internet AsLevel Ecosystem. Springer Berlin Heidelberg,
2009, p. 1605. 

Yubo Wang, Gaoxi Xiao, Jie Hu, Tee Hiang
Cheng, Limsoon Wang.
Imperfect Targeted Immunization in Scalefree Networks. Physica A
388 (2009) 25352546. 

Svante Janson Tomasz Luczak and Ilkka Norros.
Large cliques in
a powerlaw
random graph. Submitted. 2009. 
Static Network Estimate

N. Meinshausen and P. Buhlmann.
Highdimensional Graphs and Variables Selection with the Lasso.
Annals of Statistics, 34:1436, 2006. 

J. Friedman, T. Hastie, and R. Tibshirani.
Sparse
Inverse Covariance Estimation with the Graphical Lasso. Biostat,
page kxm045, 2007b. 

Duchi, S. Gould, and D. Koller.
Projected Subgradient Methods for Learning Sparse Gaussians. In
Proceedings of the Twentyfourth Conference on Uncertainty in AI (UAI),
2008a. 

P. Ravikumar, M.J. Wainwright, G. Rashutti, and B.Yu,
Highdimensional Covariance Estimation by Minimizing l1penalized
logdeterminant Divergence. Nov.2008. 

Alessandro Ferrante and Gopal Pandurangan and
Kihong Park. On the
Hardness of Optimization in Power Law source. Theoretical Computer
Science 393 (2008) 220230. 

J. Fan, Y. Feng, and Y. Wu.
Network
exploration via the adaptive lasso and scad penalties. Annals of
Applied Statistics. Submitted. 
Timevarying Network Estimate

N. Luscombe, M. Babu, H. Yu, M. Snyder, S. Teichmann, and M. Gerstein.
Genomic Analysis of Regulatory Network Dynamics Reveals Large
Topological Changes. Nature, 431:308312, 2004. 

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. 

Guo, S. Hanneke, W. Fu, and E.P.Xing.
Recovering Temporally Rewiring Networks: A Modelbased Approach.
International Conference of Machine Learning, 2007. 

O. Banerjee, L. El Ghaoui, and A. d'Aspremont.
Model Selection Through Sparse Maximum Likelihood Estimation. J.
Mach. Learn. Res., 9:485516, 2008. 

J. Peng, P. Wang, N. Zhou, and J. Zhu.
Partial Correlation Estimation by Joint Sparse Regression models.
2008. 

M. Kolar, L. Song, A. Ahmed, and E. P. Xing,
Estimating TimeVarying Networks , Annals of Applied Statistics,
2009. (earlier version appeared in arXiv:0812.5087)


M. Kolar, L. Song and E. P. Xing,
Sparsistent Learning of Varyingcoefficient Models with Structural
Changes, Proceeding of the 23rd Neural Information Processing
Systems, (NIPS 2009). 

L. Song, M. Kolar and E. P. Xing,
TimeVarying Dynamic Bayesian Networks, Proceeding of the 23rd
Neural Information Processing Systems, (NIPS 2009).


E.P. Xing, W. Fu, and L. Song,
A StateSpace Mixed Membership Blockmodel for Dynamic Network Tomography,
Annals of Applied Statistics, 2009. (earlier version appeared in
arXiv:0901.0135) 
Community structure Detection

L. Donetti and M. A. Muńoz,
Detecting
Network Communities: a new systematic and powerful algorithm, J.
Stat. Mech.: Theor. Exp. 2004 [Spectral] 

M. J. Newman,
Modularity and
community structure in networks, PNAS, 2006 [Spectral] 

Martin Rosvall and Carl T. Bergstrom,
An informationtheoretic
framework for resolving community structure in complex networks, PNAS,
2006 [MDL] 

Martin Rosvall and Carl T. Bergstrom,
Maps of random
walks on complex networks reveal community structure, PNAS, 2008 [InfoMap] 

J.P. Onnela, J. Saramäki , J. Kertész, K.
Kaski,
Intensity and coherence of motifs in weighted complex networks, Phys.
Rev. E 71, 2005 [CPMw] 

Peter Ronhovde and Zohar Nussinov,
Local resolutionlimitfree Potts
model for community detection, Phys. Rev. E 81, 046114, 2010 [Pott
Model] 
Specific Types of
Networks
Biological Networks

KuangChi Chen,
TseYi Wang,
HueiHun Tseng,
ChiYing F. Huang, and
ChengYan Kao.
A stochastic differential equation model for quantifying transcriptional
regulatory network in Saccharomyces cerevisiae.
Bioinformatics, Volume 21, Number 12, 15 June 2005. p28832890. 

Chang YH, Wang YC, Chen BS.
Identification of transcription factor cooperativity via stochastic system
model. Bioinformatics 2006 , 22(18):22762282. 

Adriana ClimescuHaulica and Michelle D
Quirk. A
stochastic differential equation model for transcriptional regulatory
networks. BMC Bioinformatics.
2007;
8(Suppl
5): S4. 

RuiSheng Wang, XiangSun Zhang and Luonan Chen.
Inferring Transcriptional Interactions and regulator Activities from
Experimental Data. Molecules and Cells. 31 December 2007 Volume 24,
Number 3, pp. 307315. 


Brain's Network
Cognitive
Networks
Communication Networks
Cyberphysical System
Economic Networks
Power Grid

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. 19201941. 

Marija D. Ilic.
From Hierarchical to Open Access Electric Power Systems. Proceedings
of the IEEE, 95(5) May 2007,10601084.


List of papers on
Cascading Failure on
Power grids. 

List of papers on
Cascading Failure on Complex networks. 

Books on Power grid: 

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

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

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

David Kempe, Jon Kleinberg, Éva Tardos.
Maximizing
the Spread of Influence through a Social Network. Kempe, Jon
Kleinberg, Éva Tardos. KDD 2003. p137146.


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 ACMSIAM symposium on Discrete
algorithms. New Orleans,
Louisiana. p718  727. 2004. 

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),
p11271138. 

Elchanan Mossel, Sebastien Roch.
On the submodularity of influence in social networks.
Annual ACM Symposium on Theory of
Computing, Proceedings of the thirtyninth annual ACM symposium on Theory
of computing. San Diego,
California, USA. SESSION: Session 3B. p128  134. 2007. 

Ning Chen.
On the
approximability of influence in social networks. SIAM Journal on
Discrete Mathematics archive. Volume 23 , Issue 3 (July 2009). 

http://en.wikipedia.org/wiki/Social_networks 

http://sspnet.eu/ 
Transportation Networks
Curriculum for
Training Network Science Researcher
click here.
Useful links


biological networks
social networks
economic
networks
power grid
communication networks
transportation networks 