Complex network
In the context of network theory, a complex network is a graph (network) with nontrivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in graphs modelling of real systems. The study of complex networks is a young and active area of scientific research[1][2][3][4] (since 2000) inspired largely by the empirical study of realworld networks such as computer networks, technological networks, brain networks and social networks.
Network science  

Network types  
Graphs  


Models  


 


Definition
Most social, biological, and technological networks display substantial nontrivial topological features, with patterns of connection between their elements that are neither purely regular nor purely random. Such features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure, and hierarchical structure. In the case of directed networks these features also include reciprocity, triad significance profile and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features. The most complex structures can be realized by networks with a medium number of interactions.[5] This corresponds to the fact that the maximum information content (entropy) is obtained for medium probabilities.
Two wellknown and much studied classes of complex networks are scalefree networks[6] and smallworld networks,[7][8] whose discovery and definition are canonical casestudies in the field. Both are characterized by specific structural features—powerlaw degree distributions for the former and short path lengths and high clustering for the latter. However, as the study of complex networks has continued to grow in importance and popularity, many other aspects of network structure have attracted attention as well.
Recently, the study of complex networks has been expanded to networks of networks.[9] If those networks are interdependent, they become significantly more vulnerable to random failures and targeted attacks and exhibit cascading failures and firstorder percolation transitions.[10]
Furthermore, the collective behavior of a network in the presence of nodes failure and recovery has been studied.[11] it was found that such a network can have spontaneous failures and spontaneous recoveries.
The field continues to develop at a brisk pace, and has brought together researchers from many areas including mathematics, physics, electric power systems,[1] biology, climate, computer science, sociology, epidemiology, and others.[12] Ideas from network science and engineering have been applied to the analysis of metabolic and genetic regulatory networks; the study of ecosystem stability and robustness;[13] clinical science;[14] the modeling and design of scalable communication networks such as the generation and visualization of complex wireless networks;[15] the development of vaccination strategies for the control of disease; and a broad range of other practical issues. Research on networks are regularly published in the most visible scientific journals and obtain vigorous funding in many countries. Network theory was found recently useful to identify bottlenecks in city traffic.[16] Network science is the topic of many conferences in a variety of different fields, and has been the subject of numerous books both for the lay person and for the expert.
Scalefree networks
A network is named scalefree[6] if its degree distribution, i.e., the probability that a node selected uniformly at random has a certain number of links (degree), follows a particular mathematical function called a power law. The power law implies that the degree distribution of these networks has no characteristic scale. In contrast, networks with a single welldefined scale are somewhat similar to a lattice in that every node has (roughly) the same degree. Examples of networks with a single scale include the Erdős–Rényi (ER) random graph, random regular graphs, regular lattices, and hypercubes. Some models of growing networks that produce scaleinvariant degree distribution are the Barabási–Albert model and the fitness model, In a network with a scalefree degree distribution, some vertices have a degree that is orders of magnitude larger than the average  these vertices are often called "hubs", although this is a bit misleading as there is no inherent threshold above which a node can be viewed as a hub. If there were such a threshold, the network would not be scalefree.
Interest in scalefree networks began in the late 1990s with the reporting of discoveries of powerlaw degree distributions in real world networks such as the World Wide Web, the network of Autonomous systems (ASs), some networks of Internet routers, protein interaction networks, email networks, etc. Most of these reported "power laws" fail when challenged with rigorous statistical testing, but the more general idea of heavytailed degree distributions—which many of these networks do genuinely exhibit (before finitesize effects occur)  are very different from what one would expect if edges existed independently and at random (i.e., if they followed a Poisson distribution). There are many different ways to build a network with a powerlaw degree distribution. The Yule process is a canonical generative process for power laws, and has been known since 1925. However, it is known by many other names due to its frequent reinvention, e.g., The Gibrat principle by Herbert A. Simon, the Matthew effect, cumulative advantage and, preferential attachment by Barabási and Albert for powerlaw degree distributions. Recently, Hyperbolic Geometric Graphs have been suggested as yet another way of constructing scalefree networks.
Some networks with a powerlaw degree distribution (and specific other types of structure) can be highly resistant to the random deletion of vertices—i.e., the vast majority of vertices remain connected together in a giant component.[17] Such networks can also be quite sensitive to targeted attacks aimed at fracturing the network quickly. When the graph is uniformly random except for the degree distribution, these critical vertices are the ones with the highest degree, and have thus been implicated in the spread of disease (natural and artificial) in social and communication networks, and in the spread of fads (both of which are modeled by a percolation or branching process). While random graphs (ER) have an average distance of order log N[7] between nodes, where N is the number of nodes, scale free graph can have a distance of log log N. Such graphs are called ultra small world networks.[18]
Smallworld networks
A network is called a smallworld network[7] by analogy with the smallworld phenomenon (popularly known as six degrees of separation). The small world hypothesis, which was first described by the Hungarian writer Frigyes Karinthy in 1929, and tested experimentally by Stanley Milgram (1967), is the idea that two arbitrary people are connected by only six degrees of separation, i.e. the diameter of the corresponding graph of social connections is not much larger than six. In 1998, Duncan J. Watts and Steven Strogatz published the first smallworld network model, which through a single parameter smoothly interpolates between a random graph and a lattice.[7] Their model demonstrated that with the addition of only a small number of longrange links, a regular graph, in which the diameter is proportional to the size of the network, can be transformed into a "small world" in which the average number of edges between any two vertices is very small (mathematically, it should grow as the logarithm of the size of the network), while the clustering coefficient stays large. It is known that a wide variety of abstract graphs exhibit the smallworld property, e.g., random graphs and scalefree networks. Further, real world networks such as the World Wide Web and the metabolic network also exhibit this property.
In the scientific literature on networks, there is some ambiguity associated with the term "small world". In addition to referring to the size of the diameter of the network, it can also refer to the cooccurrence of a small diameter and a high clustering coefficient. The clustering coefficient is a metric that represents the density of triangles in the network. For instance, sparse random graphs have a vanishingly small clustering coefficient while real world networks often have a coefficient significantly larger. Scientists point to this difference as suggesting that edges are correlated in real world networks.
See also
Books
 B. S. Manoj, Abhishek Chakraborty, and Rahul Singh, Complex Networks: A Networking and Signal Processing Perspective, Pearson, New York, USA, February 2018. ISBN 9780134786995
 S.N. Dorogovtsev and J.F.F. Mendes, Evolution of Networks: From biological networks to the Internet and WWW, Oxford University Press, 2003, ISBN 0198515901
 Duncan J. Watts, Six Degrees: The Science of a Connected Age, W. W. Norton & Company, 2003, ISBN 0393041425
 Duncan J. Watts, Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton University Press, 2003, ISBN 0691117047
 AlbertLászló Barabási, Linked: How Everything is Connected to Everything Else, 2004, ISBN 0452284392
 Alain Barrat, Marc Barthelemy, Alessandro Vespignani, Dynamical processes on complex networks, Cambridge University Press, 2008, ISBN 9780521879507
 Stefan Bornholdt (Editor) and Heinz Georg Schuster (Editor), Handbook of Graphs and Networks: From the Genome to the Internet, 2003, ISBN 3527403361
 Guido Caldarelli, ScaleFree Networks, Oxford University Press, 2007, ISBN 9780199211517
 Guido Caldarelli, Michele Catanzaro, Networks: A Very Short Introduction Oxford University Press, 2012, ISBN 9780199588077
 E. Estrada, "The Structure of Complex Networks: Theory and Applications", Oxford University Press, 2011, ISBN 9780199591756
 Reuven Cohen and Shlomo Havlin, Complex Networks: Structure, Robustness and Function, Cambridge University Press, 2010, ISBN 9780521841566
 Mark Newman, Networks: An Introduction, Oxford University Press, 2010, ISBN 9780199206650
 Mark Newman, AlbertLászló Barabási, and Duncan J. Watts, The Structure and Dynamics of Networks, Princeton University Press, Princeton, 2006, ISBN 9780691113579
 R. PastorSatorras and A. Vespignani, Evolution and Structure of the Internet: A statistical physics approach, Cambridge University Press, 2004, ISBN 0521826985
 T. Lewis, Network Science, Wiley 2009,
 Niloy Ganguly (Editor), Andreas Deutsch (Editor) and Animesh Mukherjee (Editor), Dynamics On and Of Complex Networks Applications to Biology, Computer Science, and the Social Sciences, 2009, ISBN 9780817647506
 Vito Latora, Vincenzo Nicosia, Giovanni Russo, Complex Networks: Principles, Methods and Applications, Cambridge University Press, 2017, ISBN 9781107103184
References
 Saleh, Mahmoud; Esa, Yusef; Mohamed, Ahmed (20180529). "Applications of Complex Network Analysis in Electric Power Systems". Energies. 11 (6): 1381. doi:10.3390/en11061381.
 Stephenson, C.; et., al. (2017). "Topological properties of a selfassembled electrical network via ab initio calculation". Scientific Reports. 7: 41621. Bibcode:2017NatSR...741621S. doi:10.1038/srep41621. PMC 5290745. PMID 28155863.
 Lu, J.; et., al. (2013). "Theory and applications of complex networks: Advances and challenges". 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013). pp. 2291–2294. doi:10.1109/ISCAS.2013.6572335. ISBN 9781467357623.
 Peng, Minfang (2011). "Complex network application in fault diagnosis of analog circuits". Int. J. Bifurc. Chaos. 21 (5): 1323–1330. Bibcode:2011IJBC...21.1323P. doi:10.1142/S0218127411029185.
 T. Wilhelm, J. Kim (2008). "What is a complex graph?". Physica A. 387 (11): 2637–2652. Bibcode:2008PhyA..387.2637K. doi:10.1016/j.physa.2008.01.015.
 A. Barabasi, E. Bonabeau (2003). "ScaleFree Networks". Scientific American: 50–59.
 S. H. Strogatz, D. J. Watts (1998). "Collective dynamics of 'smallworld' networks". Nature. 393 (6684): 440–442. Bibcode:1998Natur.393..440W. doi:10.1038/30918. PMID 9623998.
 H.E. Stanley, L.A.N. Amaral, A. Scala, M. Barthelemy (2000). "Classes of smallworld networks". PNAS. 97 (21): 11149–52. arXiv:condmat/0001458. Bibcode:2000PNAS...9711149A. doi:10.1073/pnas.200327197. PMC 17168. PMID 11005838.
 Buldyrev, Sergey V.; Parshani, Roni; Paul, Gerald; Stanley, H. Eugene; Havlin, Shlomo (2010). "Catastrophic cascade of failures in interdependent networks". Nature. 464 (7291): 1025–1028. arXiv:0907.1182. Bibcode:2010Natur.464.1025B. doi:10.1038/nature08932. ISSN 00280836. PMID 20393559.
 Parshani, Roni; Buldyrev, Sergey V.; Havlin, Shlomo (2010). "Interdependent Networks: Reducing the Coupling Strength Leads to a Change from a First to Second Order Percolation Transition". Physical Review Letters. 105 (4): 048701. arXiv:1004.3989. Bibcode:2010PhRvL.105d8701P. doi:10.1103/PhysRevLett.105.048701. ISSN 00319007. PMID 20867893.
 Majdandzic, Antonio; Podobnik, Boris; Buldyrev, Sergey V.; Kenett, Dror Y.; Havlin, Shlomo; Eugene Stanley, H. (2013). "Spontaneous recovery in dynamical networks". Nature Physics. 10 (1): 34–38. Bibcode:2014NatPh..10...34M. doi:10.1038/nphys2819. ISSN 17452473.
 A.E. Motter, R. Albert (2012). "Networks in Motion". Physics Today. 65 (4): 43–48. arXiv:1206.2369. Bibcode:2012PhT....65d..43M. doi:10.1063/pt.3.1518.
 Johnson S, Domı́nguezGarcı́a V, Donetti L, Muñoz MA (2014). "Trophic coherence determines foodweb stability". Proc Natl Acad Sci USA. 111 (50): 17923–17928. arXiv:1404.7728. Bibcode:2014PNAS..11117923J. doi:10.1073/pnas.1409077111. PMC 4273378. PMID 25468963.CS1 maint: multiple names: authors list (link)
 S.G.Hofmann, J.E.Curtiss (2018). "A complex network approach to clinical science". European Journal of Clinical Investigation. 48 (8): e12986. doi:10.1111/eci.12986. PMID 29931701.
 Mouhamed Abdulla (20120922). On the Fundamentals of Stochastic Spatial Modeling and Analysis of Wireless Networks and its Impact to Channel Losses. Ph.D. Dissertation, Dept. Of Electrical and Computer Engineering, Concordia Univ., Montréal, Québec, Canada, Sep. 2012. (phd). Concordia University. pp. (Ch.4 develops algorithms for complex network generation and visualization).
 Li, Daqing; Fu, Bowen; Wang, Yunpeng; Lu, Guangquan; Berezin, Yehiel; Stanley, H. Eugene; Havlin, Shlomo (2015). "Percolation transition in dynamical traffic network with evolving critical bottlenecks". Proceedings of the National Academy of Sciences. 112 (3): 669–672. Bibcode:2015PNAS..112..669L. doi:10.1073/pnas.1419185112. ISSN 00278424. PMC 4311803. PMID 25552558.
 Cohen, Reuven; Erez, Keren; benAvraham, Daniel; Havlin, Shlomo (2000). "Resilience of the Internet to Random Breakdowns". Physical Review Letters. 85 (21): 4626–4628. arXiv:condmat/0007048. Bibcode:2000PhRvL..85.4626C. doi:10.1103/PhysRevLett.85.4626. ISSN 00319007. PMID 11082612.
 R. Cohen, S. Havlin (2003). "Scalefree networks are ultrasmall". Phys. Rev. Lett. 90 (5): 058701. arXiv:condmat/0205476. Bibcode:2003PhRvL..90e8701C. doi:10.1103/physrevlett.90.058701. PMID 12633404.
 D. J. Watts and S. H. Strogatz (1998). "Collective dynamics of 'smallworld' networks". Nature. 393 (6684): 440–442. Bibcode:1998Natur.393..440W. doi:10.1038/30918. PMID 9623998.
 S. H. Strogatz (2001). "Exploring Complex Networks". Nature. 410 (6825): 268–276. Bibcode:2001Natur.410..268S. doi:10.1038/35065725. PMID 11258382.
 R. Albert and A.L. Barabási (2002). "Statistical mechanics of complex networks". Reviews of Modern Physics. 74 (1): 47–97. arXiv:condmat/0106096. Bibcode:2002RvMP...74...47A. doi:10.1103/RevModPhys.74.47.
 S. N. Dorogovtsev and J.F.F. Mendes (2002). "Evolution of Networks". Adv. Phys. 51 (4): 1079–1187. arXiv:condmat/0106144. Bibcode:2002AdPhy..51.1079D. doi:10.1080/00018730110112519.
 M. E. J. Newman, The structure and function of complex networks, SIAM Review 45, 167256 (2003)
 S. N. Dorogovtsev, A. V. Goltsev, and J. F. F. Mendes, Critical phenomena in complex networks, Rev. Mod. Phys. 80, 1275, (2008)
 G. Caldarelli, R. Marchetti, L. Pietronero, The Fractals Properties of Internet, Europhysics Letters 52, 386 (2000). https://arxiv.org/abs/condmat/0009178. DOI: 10.1209/epl/i2000004508
 R. Cohen, K. Erez, D. benAvraham, S. Havlin, "Resilience of the Internet to random breakdown" Phys. Rev. Lett. 85, 4626 (2000). https://arxiv.org/abs/1004.3989
 R. Cohen, K. Erez, D. benAvraham, S. Havlin, "Breakdown of the Internet under intentional attack" Phys. Rev. Lett. 86, 3682 (2001)
 R. Cohen, S. Havlin, "Scalefree networks are ultrasmall" Phys. Rev. Lett. 90, 058701 (2003)
 A. E. Motter (2004). "Cascade control and defense in complex networks". Phys. Rev. Lett. 93 (9): 098701. arXiv:condmat/0401074. Bibcode:2004PhRvL..93i8701M. doi:10.1103/PhysRevLett.93.098701.
J. Lehnert, Controlling Synchronization Patterns in Complex Networks, springer 2016