Skip to main content Accessibility help
×
  • Cited by 244
Publisher:
Cambridge University Press
Online publication date:
October 2017
Print publication year:
2017
Online ISBN:
9781316216002

Book description

Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, mathematics, engineering, biology, neuroscience and the social sciences.

Reviews

'This is a substantial text which will serve a broad section of readers who wish to gain insights into complex networks. Some effort will be needed to get the most out of this book but the reader who expends that effort will be well-rewarded. In turn, the authors are to be congratulated for the effort that they have made to produce such a delightful text.'

K. Alan Shore Source: Contemporary Physics

'Thanks to its colloquial style, the extensive use of examples and the accompanying software tools and network data sets, this book is the ideal university-level textbook for a first module on complex networks. It can also be used as a comprehensive reference for researchers in mathematics, physics, engineering, biology and social sciences, or as a historical introduction to the main findings of one of the most active interdisciplinary research fields of the moment.'

Source: Mathematical Reviews Clippings

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Contents

References
[1] T., Achacoso and W., Yamamoto. Ay's Neuroanatomy of C. elegans for Computation. CRC Press, 1991.
[2] C., Aicher, A.Z., Jacobs and A., Clauset. “Learning latent block structure in weighted networks”. J. Complex Netw. 3 (2015), 221–248.
[3] R., Albert and A.-L., Barabási. “Statistical mechanics of complex networks”. Rev. Mod. Phys. 74 (2002), 47.
[4] R., Albert and A.-L., Barabási. “Topology of evolving networks: local events and universality”. Phys. Rev. Lett. 85 (2000), 5234–5237.
[5] R., Albert, H., Jeong and A.-L., Barabási. “Internet: diameter of the World-Wide Web”. Nature 401 (1999), 130–131.
[6] E., Almaas, P., Krapivsky and S., Redner. “Statistics of weighted treelike networks”. Phys. Rev. E 71 (2005), 036124.
[7] N., Alon, R., Yuster and U., Zwick. “Finding and counting given length cycles”. Algorithmica 17 (1997), 209–223.
[8] U., Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC Mathematical and Computational Biology. Taylor & Francis, 2006.
[9] L.A.N., Amaral et al. “Classes of small-world networks”. P. Natl. Acad. Sci. USA 97 (2000), 11149–11152.
[10] T., Antal and P., Krapivsky. “Weight-driven growing networks”. Phys. Rev. E 71 (2005), 026103.
[11] A., Arenas, A., Fernández and S., Gómez. “Analysis of the structure of complex networks at different resolution levels”. New J. Phys. 10 (2008), 053039.
[12] A., Arenas et al. “Size reduction of complex networks preserving modularity”. New J. Phys. 9 (2007), 176.
[13] S., Assenza et al. “Emergence of structural patterns out of synchronization in networks with competitive interactions”. Sci. Rep. 1 (2011), 99.
[14] M., Baiesi and M., Paczuski. “Complex networks of earthquakes and aftershocks”. Nonlinear Proc. Geoph. 12 (2005), 1–11.
[15] P., Ball. “Prestige is factored into journal ratings”. Nature 439 (2006), 770–771.
[16] Y., Bar-Yam. Dynamics of Complex Systems. Westview Press, 2003.
[17] A.-L., Barabási. “Network science: luck or reason”. Nature 489 (2012), 507–508.
[18] A., Barabási et al. “Evolution of the social network of scientific collaborations”. Physica A 311 (2002), 590–614.
[19] A.-L., Barabási and R., Albert. “Emergence of scaling in random networks”. Science 286 (1999), 509–512.
[20] A.-L., Barabási, R., Albert and H., Jeong. “Mean-field theory for scale-free random networks”. Physica A 272 (1999), 173–187.
[21] A., Barrat and M., Weigt. “On the properties of small-world network models”. Eur. Phys. J. B 13 (2000), 547–560.
[22] A., Barrat et al. “The architecture of complex weighted networks”. P. Natl. Acad. Sci. USA 101 (2004), 3747–3752.
[23] A., Barrat, M., Barthélemy and A., Vespignani. “Modeling the evolution of weighted networks”. Phys. Rev. E 70 (2004), 066149.
[24] A., Barrat, M., Barthélemy and A., Vespignani. “Weighted Evolving Networks: Coupling Topology and Weight Dynamics”. Phys. Rev. Lett. 92 (2004), 228701.
[25] A., Barrat and R., Pastor-Satorras. “Rate equation approach for correlations in growing network models”. Phys. Rev. E 71 (2005), 036127.
[26] M., Barthélemy. “Spatial networks”. Phys. Rep. 499 (2011), 1–101.
[27] M., Barthélemy and L.A.N., Amaral. “Small-world networks: evidence for a crossover picture”. Phys. Rev. Lett. 82 (1999), 3180–3183.
[28] M., Barthélemy et al. “Characterization and modeling of weighted networks”. Physica A 346 (2005), 34–43.
[29] D.S., Bassett et al. “Adaptive reconfiguration of fractal small-world human brain functional networks”. P. Natl. Acad. Sci. USA 103 (2006), 19518–19523.
[30] E.T., Bell. “Exponential numbers”. Am. Math. Mon. 41 (1934), 411–419.
[31] E.A., Bender and E.R., Canfield. “The asymptotic number of labeled graphs with given degree sequences”. J. Comb. Theory A 24 (1978), 296.
[32] N., Berger et al. “Competition-induced preferential attachment”. English. Automata, Languages and Programming. Ed. by J. Díaz et al. Vol. 3142. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004, pp. 208–221.
[33] G., Bianconi. “Emergence of weight-topology correlations in complex scale-free networks”. EPL-Europhys. Lett. 71 (2005), 1029–1035.
[34] G., Bianconi and A.-L., Barabási. “Competition and multiscaling in evolving networks”. EPL-Europhys. Lett. 54 (2001), 436.
[35] G., Bianconi and A.-L., Barabási. “Bose-Einstein condensation in complex networks”. Phys. Rev. Lett. 86 (2001), 5632–5635.
[36] G., Bianconi, G., Caldarelli and A., Capocci. “Loops structure of the Internet at the autonomous system level”. Phys. Rev. E 71 (2005), 066116.
[37] G., Bianconi and M., Marsili. “Loops of any size and Hamilton cycles in random scale-free networks”. J, Stat. Mech.-Theory E. 2005 (2005), P06005.
[38] J.J., Binney et al. The Theory of Critical Phenomena: An Introduction to the Renormalization Group. New York, NY: Oxford University Press, Inc., 1992.
[39] P., Blanchard and D., Volchenkov. Mathematical Analysis of Urban Spatial Networks. Springer complexity. Springer, 2009.
[40] V. D, Blondel. et al. “Fast unfolding of communities in large networks”. J, Stat. Mech.-Theory E. 2008 (2008), P10008.
[41] S., Boccaletti, D.-U., Hwang and V., Latora. “Growing hierarchical scale-free networks by means of non-hierarchical processes”. Int. J. Bifurcat. Chaos 17 (2007), 2447–2452.
[42] S., Boccaletti, V., Latora and Y., Moreno. Handbook on Biological Networks. World Scientific Lecture Notes in Complex Systems. World Scientific Publishing Company, Incorporated, 2009.
[43] S., Boccaletti et al. “Complex networks: structure and dynamics”. Phys. Rep. 424 (2006), 175–308.
[44] N., Boccara. Modeling Complex Systems. New York: Springer-Verlag, 2004.
[45] M., Boguñá, R., Pastor-Satorras and A., Vespignani. “Cut-offs and finite size effects in scale-free networks”. Eur. Phys. J. B 38 (2004), 205–209.
[46] M., Boguñá and R., Pastor-Satorras. “Class of correlated random networks with hidden variables”. Phys. Rev. E 68 (2003), 036112.
[47] B., Bollob>ás. Modern Graph Theory. Corrected. Springer, 1998.
[48] B., Bollobás. Random Graphs. Cambridge studies in advanced mathematics. Academic Press, 1985.
[49] B., Bollobás. Random Graphs. Cambridge University Press, 2001.
[50] B., Bollobás and O., Riordan. “The diameter of a scale-free random graph”. Combinatorica 24 (2004), 5–34.
[51] M., Bóna. A Walk Through Combinatorics: An Introduction to Enumeration and Graph Theory. New Jersey: World Scientific Pub. cop., 2006.
[52] P., Bonacich. “Factoring and weighting approaches to status scores and clique identification”. J. Math. Sociol. 2 (1972), 113–120.
[53] P., Bonacich and P., Lloyd. “Eigenvector-like measures of centrality for asymmetric relations”. Soc. Networks 23 (2001), 191–201.
[54] G., Bonanno, F., Lillo and R., Mantegna. “High-frequency cross-correlation in a set of stocks”. Quant. Financ. 1 (2001), 96–104.
[55] J.-A. Bondy and U.S.R., Murty. Graph Theory. Graduate texts in mathematics. OHX. New York, London: Springer, 2007.
[56] U., Brandes et al. “Maximizing Modularity is hard” (2007).
[57] U., Brandes. “A Faster Algorithm for Betweenness Centrality”. J. Math. Sociol. 25 (2001), 163–177.
[58] U., Brandes and T., Erlebach. Network Analysis: Methodological Foundations. Vol. 3418. World Scientific Lecture Notes in Complex Systems. Lecture Notes in Computer Science Tutorial, Springer-Verlag, 2005.
[59] A., Broder et al. “Graph structure in the web”. Comput. Netw. 33 (2000), 309–320.
[60] J., Buhl et al. “Efficiency and robustness in ant networks of galleries”. Eur. Phys. J. B 42 (2004), 123–129.
[61] E., Bullmore and O., Sporns. “Complex brain networks: graph theoretical analysis of structural and functional systems”. Nat. Rev. Neurosci. 10 (2009), 186–198.
[62] E., Bullmore and O., Sporns. “The economy of brain network organization”. Nat. Rev. Neurosci. (2012), 336–349.
[63] R., Burt. Structural Holes. Harvard University Press, 1995.
[64] G., Caldarelli. Scale-Free Networks: Complex Webs in Nature and Technology. Oxford Finance Series. Oxford: Oxford University Press, 2007.
[65] G., Caldarelli et al. “Scale-free networks from varying vertex intrinsic fitness”. Phys. Rev. Lett. 89 (2002), 258702.
[66] D.S., Callaway et al. “Are randomly grown graphs really random?Phys. Rev. E 64 (2001), 041902.
[67] R.F., Cancho and R. V., Solé. “Optimization in complex networks”. Lect. Notes Phys. (2003), 114–126.
[68] A., Cardillo, S., Scellato and V., Latora. “A topological analysis of scientific coauthorship networks”. Physica A 372 (2006), 333–339.
[69] A., Cardillo et al. “Structural properties of planar graphs of urban street patterns”. Phys. Rev. E 73 (2006), 066-107.
[70] C., Caretta Cartozo and P. De Los Rios. “Extended navigability of small world networks: exact results and new insights”. Phys. Rev. Lett. 102 (2009), 238703.
[71] S., Carmi et al. “Asymptotic behavior of the Kleinberg model”. Phys. Rev. Lett. 102 (2009), 238702.
[72] M., Catanzaro, M., Boguñá and R., Pastor-Satorras. “Generation of uncorrelated random scale-free networks”. Phys. Rev. E 71 (2005), 027103.
[73] M., Chavez et al. “Functional modularity of background activities in normal and epileptic brain networks”. Phys. Rev. Lett. 104 (2010), 118701.
[74] P., Chen et al. “Finding scientific gems with Google's PageRank algorithm”. J. Informetr. 1 (2007), 8–15.
[75] T., Chow. Mathematical Methods for Physicists: A Concise Introduction. Cambridge University Press, 2000.
[76] F., Chung and L., Lu. “The average distances in random graphs with given expected degrees”. P. Natl. Acad. Sci. USA 99 (2002), 15879.
[77] F., Chung and L., Lu. “The diameter of sparse random graphs”. Adv. Appl. Math 26 (2001), 257–279.
[78] V., Ciotti et al. “Homophily and missing links in citation networks”. Eur. Phys. J. Data Sci. 5 (2016).
[79] A., Clauset, M.E.J., Newman and C., Moore. “Finding community structure in very large networks”. Phys. Rev. E 70 (2004), 066111.
[80] A., Clauset, C. R., Shalizi and M.E.J., Newman. “Power-law distributions in empirical data”. SIAM Rev. 51, (2007), 661–703.
[81] J., R. Clough et al. “Transitive reduction of citation networks”. J. Complex Netw. 3 (2015), 189–203.
[82] R., Cohen and S., Havlin. “Scale-free networks are ultrasmall”. Phys. Rev. Lett. 90 (2003).
[83] V., Colizza et al. “Detecting rich-club ordering in complex networks”. Nat. Phys. 2 (2006), 110–115.
[84] V., Colizza, R., Pastor-Satorras and A., Vespignani. “Reaction-diffusion processes and metapopulation models in heterogeneous networks”. Nat. Phys. 3 (2007), 276–282.
[85] A., Condon and R. M., Karp. “Algorithms for graph partitioning on the planted partition model”. Random Struct. Algor. 18 (2001), 116–140.
[86] T. H., Cormen. et al. Introduction to Algorithms. MIT Press, 2001.
[87] B., Cronin. The Citation Process. The Role and Significance of Citations in Scientific Communication. London: Taylor Graham, 1984.
[88] P., Crucitti, V., Latora and S., Porta. “Centrality in networks of urban streets”. Chaos 16 (2006), 015113.
[89] P., Crucitti, V., Latora and S., Porta. “Centrality measures in spatial networks of urban streets”. Phys. Rev. E 73 (2006), 036125.
[90] L., Danon et al. “Comparing community structure identification”. J. Stat. Mech. Theory E. 2005 (2005), P09008.
[91] E., David and K., Jon. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. New York, NY: Cambridge University Press, 2010.
[92] F., De Vico Fallani et al. “Graph analysis of functional brain networks: practical issues in translational neuroscience”. Phylos. T. R. Soc. B 369 (2014).
[93] M., T Dickerson. et al. “Fast greedy triangulation algorithms”. Comp. Geom.-Theor. Appl. 8 (1997), 67–86.
[94] E., W. Dijkstra. “A note on two problems in connexion with graphs”. Num. Math. 1 (1959), 269–271.
[95] P., S. Dodds. “An experimental study of search in global social networks”. Science 301 (2003), 827–829.
[96] S., N Dorogovtsev. and J.F.F., Mendes “Minimal models of weighted scale-free networks” arXiv:cond-mat/0408343.
[97] S., N Dorogovtsev., J.F.F., Mendes and A. N., Samukhin. “Structure of growing networks with preferential linking”. Phys. Rev. Lett. 85 (2000), 4633–4636.
[98] R., M. D'Souza et al. “Emergence of tempered preferential attachment from optimization”. P. Natl. Acad. Sci. USA 104 (2007), 6112–6117.
[99] R., Durrett. Random Graph Dynamics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 2010.
[100] P., Erdős and A., Rényi. “On random graphs I”. Publ. Math.-Debrecen 6 (1959), 290.
[101] P., Erdős and A., Rényi. “On the evolution of random graphs”. Publ. Math. Inst. Hungary. Acad. Sci. 5 (1960), 17–61.
[102] E., Estrada. The Structure of Complex Networks: Theory and Applications. New York, NY: Oxford University Press, Inc., 2011.
[103] E., Estrada and N., Hatano. “Communicability in complex networks”. Phys. Rev. E 77 (2008), 036111.
[104] E., Estrada and J. A. Rodríguez-Velázquez. “Subgraph centrality in complex networks”. Phys. Rev. E 71 (2005), 056103.
[105] L., Euler. “Solutio problematis ad geometriam situs pertinentis”. Comment. Acad. Sci. U. Petrop. 8 (1736), 128–140.
[106] J., A. Evans. “Future science”. Science 342 (2013), 44–45.
[107] T., S. Evans and J. P., Saramäki. “Scale-free networks from self-organization”. Phys. Rev. E 72 (2005), 026138.
[108] M., G. Everett and S. P., Borgatti. “The centrality of groups and classes”. J. Math. Sociol. 23 (1999), 181–201.
[109] A., Fabrikant, E., Koutsoupias and C. H., Papadimitriou. “Heuristically optimized trade-offs: a new paradigm for power laws in the Internet”. Proceedings of the 29th International Colloquium on Automata, Languages and Programming. ICALP ’02. London, UK: Springer-Verlag, 2002, pp. 110–122. 540 References
[110] G., Fagiolo, J., Reyes and S., Schiavo. “World-trade web: topological properties, dynamics, and evolution”. Phys. Rev. E 79 (2009), 036115.
[111] K., Falconer. Fractal Geometry: Mathematical Foundations and Applications. 2nd Ed. Wiley, 2003.
[112] F., D. V., Fallani et al. “Defecting or not defecting: how to ‘read’ human behavior during cooperative games by EEG measurements”. PLoS ONE 5(12): (2011), 5:e14187(2010).
[113] F., D. V., Fallani and F., Babiloni. “The graph theoretical approach in brain functional networks: theory and applications”. Synthesis Lect. Biomed. Eng. 5 (2010), 1–92.
[114] S., L. Feld. “The focused organization of social ties”. Am. J. Sociol. 86 (1981), 1015– 1035.
[115] S., L. Feld. “Why your friends have more friends than you do”. Am. J. Sociol. 96 (1991), 1464–1477.
[116] M., Fiedler. “Algebraic connectivity of graphs”. Czech. Math. J. 23 (1973), 298–305.
[117] R., A. Fisher. “The use of multiple measurements in taxonomic problems”. Ann. Eugenic. 7 (1936), 179–188.
[118] G., S. Fishman. “Sampling from the Poisson distribution on a computer”. Computing 17 (1976), 147–156.
[119] S., Fortunato. “Community detection in graphs”. Phys. Rep. 486, (2009), 75–174.
[120] S., Fortunato and M., Barthélemy. “Resolution limit in community detection”. P. Natl. Acad. Sci. USA 104 (2007), 36–41.
[121] H., Frank and W., Chou. “Connectivity considerations in the design of survivable networks”. IEEE T. Circuits Syst. 17 (1970), 486–490.
[122] L., Freeman. “A set of measures of centrality based on betweenness”. Sociometry (1977).
[123] L., Freeman. “Centrality in social networks: conceptual clarification”. Soc. Networks 1 (1979), 215–239.
[124] L., C. Freeman, S. P., Borgatti and D. R., White. “Centrality in valued graphs: A measure of betweenness based on network flow”. Soc. Networks 13 (1991), 141– 154.
[125] G., Frobenius. “Über Matrizen aus nicht negativen Elementen”. S.-B. Deutsch. Akad. Wiss. Berlin. Math-Nat. Kl., (1912), 456–477.
[126] F., Gantmacher. The Theory of Matrices. Vol. 2. New York: Chelsea Publishing Company, 1959.
[127] E., Garfield. Citation Indexing: Its Theory and Application in Science, Technology, and Humanities. Information sciences series. Isi Press, 1979.
[128] D., Garlaschelli and M., Loffredo. “Patterns of link reciprocity in directed networks”. Phys. Rev. Lett. 93 (2004), 268701.
[129] D., Garlaschelli et al. “The scale-free topology of market investments”. Physica A 350 (2005), 491–499.
[130] C.-M., Ghim et al. “Packet transport along the shortest pathways in scale-free networks”. Eur. Phys. J. B 38 (2004), 193–199.
[131] M., Girvan and M.E.J., Newman. “Community structure in social and biological networks”. P. Natl. Acad. Sci. USA 99 (2002), 7821–7826.
[132] K.-I. Goh, B., Kahng and D., Kim. “Packet transport and load distribution in scalefree network models”. Physica A 318 (2003), 72–79.
[133] K.-I. Goh, B., Kahng and D., Kim. “Universal behavior of load distribution in scalefree networks”. Phys. Rev. Lett. 87 (2001), 278701.
[134] K.-I., Goh et al. “Classification of scale-free networks”. P. Natl. Acad. Sci. USA 99 (2002), 12583–12588.
[135] K.-I., Goh et al. “Load distribution in weighted complex networks”. Phys. Rev. E 72 (2005), 017102.
[136] S., R. Goldberg, H., Anthony and T. S., EvansModelling citation networks”. Scientometrics 105 (2015), 1577–1604.
[137] G., Golub and C., Van Loan. Matrix Computations. Johns Hopkins Studies in the Mathematical Sciences. Johns Hopkins University Press, 2013.
[138] J., Gómez-Gardeñes and Y., Moreno. “From scale-free to Erdos-Rényi networks”. Phys. Rev. E 73 (2006), 056124.
[139] J., Gómez-Gardeñes and Y., Moreno. “Local versus global knowledge in the Barabasi-Albert scale-free network model”. Phys. Rev. E 69 (2004), 037103.
[140] B., H. Good, Y.-A., de Montjoye and A., Clauset. “Performance of modularity maximization in practical contexts”. Phys. Rev. E 81 (2010), 046106.
[141] S., Goss et al. “Self-organized shortcuts in the Argentine ant”. Naturwissenschaften 76 (1989), 579–581.
[142] M., S. Granovetter. “The strength of weak ties”. Am. J. Sociol. 78 (1973), 1360.
[143] C., M. Grinstead and J. L., Snell. Introduction to Probability. Providence, RI: American Mathematical Society, 1997.
[144] J., Gross and J., Yellen. Graph Theory and Its Applications, Second Edition. Textbooks in Mathematics. Taylor & Francis, 2005.
[145] J., Guare. Six Degrees of Separation: A Play. Vintage Series. Vintage Books, 1990.
[146] R., Guimerá and L.A.N., Amaral. “Cartography of complex networks: modules and universal roles”. J, Stat. Mech.-Theory E. 2005 (2005), P02001.
[147] R., Guimerá and L.A.N., Amaral. “Functional cartography of complex metabolic networks”. Nature 433 (2005), 895–900.
[148] B., Gutenberg and C., Richter. “Magnitude and energy of earthquakes”. Nature 176 (1955), 795.
[149] R., Gutiérrez et al. “Emerging meso- and macroscales from synchronization of adaptive networks”. Phys. Rev. Lett. 107 (2011), 234103.
[150] F., Harary. Graph Theory. Addison-Wesley series in mathematics. Perseus Books, 1994.
[151] D., Hicks et al. “Bibliometrics: the Leiden manifesto for research metrics”. Nature 520 (2015), 429–431.
[152] C., Hierholzer. “Über die Möglichkeit, einen Linienzug ohne Wiederholung und ohne Unterbrechung zu umfahren”. Math. Ann. 6 (1873), 30–32.
[153] B., Hillier and J., Hanson. The Social Logic of Space. Cambridge University Press, 1984.
[154] J., E. Hirsch. “An index to quantify an individual's scientific research output”. P. Natl. Acad. Sci. USA 102 (2005), 16569–16572.
[155] J.E., Hirsch. “Does the h index have predictive power?P. Natl. Acad. Sci. USA 104 (2007), 19193–19198.
[156] P., Holme and B.J., Kim. “Growing scale-free networks with tunable clustering”. Phys. Rev. E 65 (2002), 026107.
[157] J., Hopcroft and R., Tarjan. “Efficient planarity testing”. J. ACM 21 (1974), 549–568.
[158] http://geant3.archive.geant.net.
[159] http://ghr.nlm.nih.gov.
[160] http://www.caida.org.
[161] B., Hu et al. “A weighted network model for interpersonal relationship evolution”. Physica A 353 (2005), 576–594.
[162] E., Isaacson and H., Keller. Analysis of Numerical Methods. Dover Books on Mathematics Series. Dover Publications, 1994.
[163] M., Jackson. Social and Economic Networks. Princeton University Press, 2010.
[164] A., Jacobs. Great Streets. MIT Press, 1993.
[165] H., Jeong et al. “Lethality and centrality in protein networks”. Nature 411 (2001), 41–42.
[166] H., Jeong et al. “The large-scale organization of metabolic networks”. Nature 407 (2000), 651–654.
[167] B., Jiang and C., Claramunt. “Topological analysis of urban street networks”. Environ. Plann. B 31 (2004), 151–162.
[168] E., Jin, M., Girvan and M., Newman. “Structure of growing social networks”. Phys. Rev. E 64 (2001), 046132.
[169] D., B. Johnson. “Finding all the elementary circuits of a directed graph”. SIAM J. Comput. 4 (1975), 77–84.
[170] S., C. Johnson. “Hierarchical clustering schemes”. Psychometrika 32 (1967), 241–254.
[171] V., Kachitvichyanukul and B.W., Schmeiser. “Binomial random variate generation”. Commun. ACM 31 (1988), 216–222.
[172] M., Kalos and P., Whitlock. Monte Carlo Methods. Wiley, 1986.
[173] T., Kamada and S., Kawai. “An algorithm for drawing general undirected graphs”. Inform. Process. Lett. 31 (1989), 7–15.
[174] L., Katz. “A new status index derived from sociometric analysis”. English. Psychometrika 18 (1953), 39–43.
[175] M., G. Kendall. “A new measure of rank correlation”. Biometrika 30 (1938), 81–93.
[176] M., G. Kendall. Rank Correlation Methods. London, Griffin, 1970.
[177] J., M. Kleinberg. “The convergence of social and technological networks”. Commun. ACM 51 (2008), 66.
[178] J., M. Kleinberg. “The small-world phenomenon”. Proceedings of the thirty-second annual ACM symposium on Theory of computing – STOC ’00. ACM Press, 2000.
[179] J., M. Kleinberg. “Authoritative sources in a hyperlinked environment”. J. ACM 46 (1999), 604–632.
[180] J., M. Kleinberg. “Navigation in a small world”. Nature 406 (2000), 845–845.
[181] D., E. Knuth. The Art of Computer Programming, Volume I: Fundamental Algorithms, 3rd Edition. Addison-Wesley, 1997.
[182] D., E. Knuth. The Art of Computer Programming, Volume II: Seminumerical Algorithms, 3rd Edition. Addison-Wesley, 1997.
[183] D., E. Knuth. The Art of Computer Programming, Volume III: Sorting and Searching, 2nd Edition. Addison-Wesley, 1973.
[184] G., Kossinets and D.J., Watts. “Empirical analysis of an evolving social network”. Science 311 (2006), 88–90.
[185] P., Krapivsky and S., Redner. “A statistical physics perspective on Web growth”. Comput. Netw. 39 (2002), 261–276.
[186] P., Krapivsky and S., Redner. “Organization of growing random networks”. Phys. Rev. E 63 (2001), 066123.
[187] P., Krapivsky, S., Redner and F., Leyvraz. “Connectivity of growing random networks”. Phys. Rev. Lett. 85 (2000), 4629–4632.
[188] P., Krapivsky, G., Rodgers and S., Redner. “Degree distributions of growing networks”. Phys. Rev. Lett. 86 (2001), 5401–5404.
[189] V., Krebs. “Mapping networks of terrorist cells”. Connections 24 (2002), 43–52.
[190] J., B. Kruskal. “On the shortest spanning subtree of a graph and the traveling salesman problem”. P. Am. Math. Soc. 7 (1956), 48–48.
[191] J., M. Kumpula et al. “Emergence of communities in weighted networks”. Phys. Rev. Lett. 99 (2007), 228701.
[192] L., Lacasa, V., Nicosia and V., Latora. “Network structure of multivariate time series”. Sci. Rep. 5 (2015), 15508.
[193] L., Lacasa et al. “From time series to complex networks: The visibility graph”. P. Natl. Acad. Sci. USA 105 (2008), 4972–4975.
[194] L., Leydesdorff. The Challenge of Scientometrics: The Development, Measurement, and Self-Organization of Scientific Communications. Universal-Publishers, 2001.
[195] R., Lambiotte, J.C., Delvenne and M., Barahona. “Laplacian dynamics and multiscale modular structure in networks” (2008).
[196] A., Lancichinetti and S., Fortunato. “Consensus clustering in complex networks”. Sci. Rep. 2 (2012).
[197] A., Lancichinetti, S., Fortunato and F., Radicchi. “Benchmark graphs for testing community detection algorithms”. Phys. Rev. E 78 (2008), 046110.
[198] S., Lang. Linear Algebra. Springer Undergraduate Texts in Mathematics and Technology. Springer, 1987.
[199] V., Latora and M., Marchiori. “A measure of centrality based on network efficiency”. New J. Phys. 9 (2007), 188.
[200] V., Latora and M., Marchiori. “Economic small-world behavior in weighted networks”. Eur. Phys. J. B 32 (2003), 249–263.
[201] V., Latora, V., Nicosia and P., Panzarasa. “Social cohesion, structural holes, and a tale of two measures”. English. J. Stat. Phys. 151 (2013), 745–764.
[202] V., Latora and M., Marchiori. “Efficient behavior of small-world networks”. Phys. Rev. Lett. 87 (2001), 198701.
[203] V., Latora and M., Marchiori. “Vulnerability and protection of infrastructure networks”. Phys. Rev. E 71 (2005), 015103(R).
[204] V., Latora et al. “Identifying seismicity patterns leading flank eruptions at Mt. Etna Volcano during 1981–1996”. Geophys. Res. Lett. 26 (1999), 2105–2108.
[205] S., Lehmann, A.D., Jackson and B.E., Lautrup. “Measures for measures”. Nature 444 (2006), 1003–1004.
[206] J., Leskovec et al. “Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters”. Internet Math. 6 (2009), 29–123.
[207] D., Liben-Nowell et al. “Geographic routing in social networks”. P. Natl. Acad. Sci. USA 102 (2005), 11623–11628.
[208] F., Liljeros et al. “The web of human sexual contacts”. Nature 411 (2001), 907–908.
[209] M.-E., Lynall et al. “Functional connectivity and brain networks in schizophrenia”. J. Neurosci. 30 (2010), 9477–9487.
[210] I. A. S.M., Abramowitz. Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables. Dover Publications; 1965.
[211] A., Ma and R. J., Mondragón. “Rich-cores in networks”. PLoS ONE 10 (2015).
[212] A., Ma, R. J., Mondragón and V., Latora. “Anatomy of funded research in science”. P. Natl. Acad. Sci. USA 112 (2015), 14760–14765.
[213] P. J., Macdonald, E, Almaas and A.-L, Barabási. “Minimum spanning trees of weighted scale-free networks”. EPL-Europhys. Lett. 72 (2005), 308–314.
[214] S., Mangan and U., Alon. “Structure and function of the feed-forward loop network motif”. P. Natl. Acad. Sci. USA 100 (2003), 11980–11985.
[215] R., Mantegna. “Hierarchical structure in financial markets”. Eur. Phys. J. B 11 (1999), 193–197.
[216] R., Mantegna and H., Stanley. Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, 1999.
[217] M., Matsumoto and T., Nishimura. “Mersenne Twister: a 623-dimensionally equidistributed uniform pseudo-random number generator”. ACM T. Model Comput S. 8 (1998), 3–30.
[218] C.W., Miller. “Superiority of the h-index over the impact factor for physics” (2007).
[219] R., Milo et al. “Network motifs: simple building blocks of complex networks”. Science 298 (2002), 824–827.
[220] R., Milo et al. “Superfamilies of evolved and designed networks”. Science 303 (2004), 1538–1542.
[221] M., Mitrovi'c and B., Tadi'c. “Spectral and dynamical properties in classes of sparse networks with mesoscopic inhomogeneities”. Phys. Rev. E 80 (2009), 026123.
[222] M., Molloy and B., Reed. “A critical point for random graphs with a given degree sequence”. Random Struct. Algor. 6 (1995), 161–180.
[223] M., Molloy and B., Reed. “The size of the giant component of a random graph with a given degree sequence”. Comb. Probab. Comput 7 (1998), 295–305.
[224] T., Nakagaki, H., Yamada and A., Tóth. “Intelligence: maze-solving by an amoeboid organism”. Nature 407 (2000), 470–470.
[225] M., E. J., Newman. “Clustering and preferential attachment in growing networks”. Phys. Rev. E 64 (2001), 025102.
[226] M., E. J., Newman. “Fast algorithm for detecting community structure in networks”. Phys. Rev. E 69 (2004), 066133.
[227] M., E. J., Newman. “Random graphs with clustering”. Phys. Rev. Lett. 103 (2009), 058701.
[228] M. E. J., Newman and D.J., Watts. “Scaling and percolation in the small-world network model”. Phys. Rev. E 60 (1999), 7332–7342.
[229] M. E. J., Newman. “Analysis of weighted networks”. Phys. Rev. E 70 (2004), 056131.
[230] M. E. J., Newman. “Assortative mixing in networks”. Phys. Rev. Lett. 89, (2002), 208701.
[231] M. E. J., Newman. “Handbook of graphs and networks”. Wiley-VCH, 2003. Chap. Random graphs as models of networks, p. 35.
[232] M. E. J., Newman. “Mixing patterns in networks”. Phys. Rev. E 67, (2003), 026126.
[233] M. E. J., Newman. “Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality”. Phys. Rev. E 64 (2001), 016132.
[234] M. E. J., Newman. “Scientific collaboration networks. I. Network construction and fundamental results”. Phys. Rev. E 64 (2001), 016131.
[235] M. E. J., Newman. “The structure and function of complex networks”. SIAM Rev. 45, (2003), 167–256.
[236] M. E. J., Newman, A.-L, Barabási and D.J., Watts, eds. The Structure and Dynamics of Networks. Princeton studies in complexity. Princeton, Oxford: Princeton University Press, 2006.
[237] M. E. J., Newman and M., Girvan. “Finding and evaluating community structure in networks”. Phys. Rev. E 69, (2004), 026113.
[238] M. E. J., Newman, S. H, Strogatz and D.J., Watts. “Random graphs with arbitrary degree distributions and their applications”. Phys. Rev. E 64, (2001), 026118.
[239] M. E. J., Newman. Networks: An Introduction. New York, NY: Oxford University Press, Inc., 2010.
[240] M. E. J., Newman. “A measure of betweenness centrality based on random walks”. Soc. Networks 27 (2005), 39–54.
[241] M. E. J., Newman. “Communities, modules and large-scale structure in networks”. Nat. Phys. 8 (2012), 25–31.
[242] M. E. J., Newman. “Power laws, Pareto distributions and Zipf's law”. Contemp. Phys. 46 (2005), 323–351.
[243] V., Nicosia et al. “Phase transition in the economically modeled growth of a cellular nervous system”. P. Natl. Acad. Sci. USA 110 (2013), 7880–7885.
[244] V., Nicosia et al. “Controlling centrality in complex networks”. Sci. Rep. 2 (2011).
[245] E., L. N.L., Biggs and R., Wilson. Graph Theory 1736–1936. Oxford: Clarendon Press, 1976.
[246] J., Noh and H., Rieger. “Stability of shortest paths in complex networks with random edge weights”. Phys. Rev. E 66 (2002), 066127.
[247] J.-P., Onnela et al. “Dynamics of market correlations: Taxonomy and portfolio analysis”. Phys. Rev. E 68 (2003), 056110.
[248] J.-P., Onnela et al. “Intensity and coherence of motifs in weighted complex networks”. Phys. Rev. E 71 (2005), 065103.
[249] T., Opsahl et al. “Prominence and control: the weighted rich-club effect”. Phys. Rev. Lett. 101 (2008), 168702. 546 References
[250] C., M. Papadimitriou. Computational Complexity. Reading, MA: Addison-Wesley, 1994.
[251] F., Papadopoulos et al. “Popularity versus similarity in growing networks”. Nature 489 (2012), 537–540.
[252] V., Pareto. Cours d'économie politique. Lausanne: Ed. Rouge. 1897.
[253] R., Pastor-Satorras and A., Vespignani. Evolution and Structure of the Internet: A Statistical Physics Approach. New York, NY: Cambridge University Press, 2004.
[254] R., Pastor-Satorras, A., Vazquez and A., Vespignani. “Dynamical and correlation properties of the Internet”. Phys. Rev. Lett. 87, (2001), 258701.
[255] R., Pastor-Satorras, A., Vázquez and A., Vespignani. “Topology, hierarchy, and correlations in Internet graphs”. English. Complex Networks. Ed. by E., Ben-Naim, H., Frauenfelder and Z., Toroczkai. Vol. 650. Lect. Notes Phys. Springer Berlin Heidelberg, 2004, pp. 425–440.
[256] O., Perron. “Über Matrizen”. Math. Ann. 64 (1907), 248–263.
[257] O., Persson. “Exploring the analytical potential of comparing citing and cited source items”. English. Scientometrics 68 (2006), 561–572.
[258] T., Petermann and P., De Los Rios. “Physical realizability of small-world networks”. Phys. Rev. E 73 (2006), 026114.
[259] S., Porta, P., Crucitti and V., Latora. “The network analysis of urban streets: a primal approach”. Environ. Plann. B 33 (2006), 705–725.
[260] S, Porta. et al. “Street centrality and densities of retail and services in Bologna, Italy”. Environ. Plann. B 36 (2009), 450–465.
[261] A., Pothen. Graph Partitioning Algorithms with Applications to Scientific Computing. Tech. rep. Norfolk, VA: Old Dominion University, 1997.
[262] W., H. Press et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing. 3rd ed. New York, NY: Cambridge University Press, 2007.
[263] D. D. S., Price. “A general theory of bibliometric and other cumulative advantage processes”. J. Am. Soc. Inform. Sci. 27 (1976), 292–306.
[264] F., Radicchi and C., Castellano. “Rescaling citations of publications in physics”. Phys. Rev. E 83 (2011), 046116.
[265] F., Radicchi, S., Fortunato and C., Castellano. “Universality of citation distributions: Toward an objective measure of scientific impact”. P. Natl. Acad. Sci. USA 105 (2008), 17268–17272.
[266] U. N., Raghavan, R., Albert and S., Kumara. “Near linear time algorithm to detect community structures in large-scale networks”. Phys. Rev. E 76 (2007), 036106.
[267] A., Rapoport. “Contribution to the theory of random and biased nets”. English. B. Math. Biophys. 19 (1957), 257–277.
[268] E., Ravasz et al. “Hierarchical organization of modularity in metabolic networks”. Science (New York, N.Y.) 297 (2002), 1551–1555.
[269] E., Ravasz and A.-L. L., Barabási. “Hierarchical organization in complex networks”. Phys. Rev. E 67 (2003), 026112.
[270] S., Redner. “Citation statistics from 110 years of physical review”. Phys. Today 58 (2005), 49–54.
[271] J., Reichardt and S., Bornholdt. “Statistical mechanics of community detection”. Phys. Rev. E 74 (2006), 016110.
[272] F., Roberts and B., Tesman. Applied Combinatorics, Second Edition. Titolo collana. Taylor & Francis, 2011.
[273] M., Rosvall et al. “Networks and cities: an information perspective”. Phys. Rev. Lett. 94 (2005), 028701.
[274] M., Rosvall and C.T., Bergstrom. “Maps of random walks on complex networks reveal community structure”. P. Natl. Acad. Sci. USA 105 (2008), 1118–1123.
[275] Y., Saad. Numerical Methods for Large Eigenvalue Problems. SIAM, 2011.
[276] M., Sales-Pardo et al. “Extracting the hierarchical organization of complex systems”. P. Natl. Acad. Sci. USA 104 (2007), 15224–15229.
[277] S., Scellato et al. “The backbone of a city”. English. Eur. Phys. J. B 50 (2006), 221–225.
[278] J., Scott. Social Network Analysis: A Handbook. SAGE Publications, 2000.
[279] R., Sedgewick and K., Wayne. Algorithms. Pearson Education, 2011.
[280] M., A. Serrano and M., Boguñá. “Clustering in complex networks. I. General formalism”. Phys. Rev. E 74 (2006), 056114.
[281] M., A. Serrano and M., Boguñá. “Tuning clustering in random networks with arbitrary degree distributions”. Phys. Rev. E 72 (2005), 036133.
[282] M., A. Serrano, M., Boguñá and A. Dí az, Guilera. “Competition and adaptation in an Internet evolution model”. Phys. Rev. Lett. 94 (2005), 038701.
[283] M., A. Serrano, M., Boguñá and A., Vespignani. “Extracting the multiscale backbone of complex weighted networks”. P. Natl. Acad. Sci. USA 106 (2009), 6483–6488.
[284] S., S. Shen-Orr et al. “Network motifs in the transcriptional regulation network of Escherichia coli ”. Nat. Genet. 31 (2002), 64–68.
[285] H., A. Simon. “On a class of skew distribution functions”. Biometrika 42 (1955), 425–440.
[286] P., Colomer de Simon and M., Boguñá. “Clustering of random scale-free networks”. Phys. Rev. E 86 (2012), 026120.
[287] S., Milgram. “The small world problem”. Psychol. Today 2 (1967), 60–67.
[288] R., V. Solé et al. “A model of large-scale proteome evolution”. Adv. Complex Syst. 05 (2002), 43–54.
[289] D. J., de Solla Price. “Networks of scientific papers”. Science 149 (1965), 510–515.
[290] C., Spearman. “The proof and measurement of association between two things”. Am. J. Psychol. 15 (1904), 72–101.
[291] O., Sporns. Networks of the Brain. MIT Press, 2011.
[292] H., Stanley. Introduction to Phase Transitions and Critical Phenomena. International series of monographs on physics. Oxford University Press, 1971.
[293] G., Strang. Introduction to Linear Algebra, Third Edition. Wellesley Cambridge Press, 2003.
[294] E., Strano et al. “Elementary processes governing the evolution of road networks”. Sci. Rep. 296 (2012).
[295] R., Tarjan. “Depth first search and linear graph algorithms”. SIAM J. Comput. (1972).
[296] A., Tero, R., Kobayashi and T., Nakagaki. “Physarum solver: a biologically inspired method of road-network navigation”. Physica A 363 (2006), 115–119.
[297] P., Tieri et al. “Quantifying the relevance of different mediators in the human immune cell network”. Bioinformatics 21 (2005), 1639–1643.
[298] J., Travers and S., Milgram. “An experimental study of the small world problem”. Sociometry 32 (1969), 425–443.
[299] M., Tumminello et al. “A tool for filtering information in complex systems”. P. Natl. Acad. Sci. USA 102 (2005), 10421–10426.
[300] A., M. Turing. “On computable numbers, with an application to the entscheidungsproblem”. P. Lond. Math, Soc. 42 (1936), 230–265.
[301] R., S. Varga. Matrix Iterative Analysis. Englewood Cliffs, NJ: Prentice Hall Inc., 1962.
[302] S., Varier and M., Kaiser. “Neural development features: spatio-temporal development of the Caenorhabditis elegans neuronal network”. PLoS Comput. Biol. 7 (2011). Ed. by K.J., Friston, e1001044.
[303] L., R. Varshney et al. “Structural properties of the Caenorhabditis elegans neuronal network”. PLoS Comput. Biol. 7 (2011). Ed. by O., Sporns, e1001066.
[304] A, Vazquez. “Disordered networks generated by recursive searches”. EPL-Europhys. Lett. 54 (2001), 430–435.
[305] A., Vázquez. “Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations”. Phys. Rev. E 67 (2003), 056104.
[306] A., Vázquez, R., Pastor-Satorras and A., Vespignani. “Large-scale topological and dynamical properties of Internet”. Phys. Rev. E 65, (2002), 066130.
[307] A., Vázquez et al. “Modeling of protein interaction networks”. Complexus 1 (2003), 38–44.
[308] S., Wasserman and K., Faust. Social Network Analysis: Methods and Applications. Vol. 8. Cambridge University Press, 1994.
[309] D. J., Watts. Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton, NJ: Princeton University Press, 1999, xv, 262 p.
[310] D. J., Watts. Small Worlds: The Dynamics of Networks Between Order and Randomness. Menasha, Wisc.: The Association, 2003.
[311] D. J., Watts. and S.H., Strogatz. “Collective dynamics of ‘small-world’ networks”. Nature 393 (1998), 440–442.
[312] S., Weber and M., Porto. “Generation of arbitrarily two-point-correlated random networks”. Phys. Rev. E 76 (2007), 046111.
[313] D., West. Introduction to Graph Theory. Prentice Hall PTR, 2007.
[314] J.G., White et al. “The structure of the nervous system of the nematode Caenorhabditis elegans”. Phylos. T. R. Soc. B 314 (1986), 1–340.
[315] H., Wielandt. “Unzerlegbare nicht negativen Matrizen”. Math. Z. 52 (1950), 642–648.
[316] C.R., Woese, O., Kandler and M.L., Wheelis. “Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya”. P. Natl. Acad. Sci. USA 87 (1990), 4576–4579.
[317] R., Xulvi-Brunet and I.M., Sokolov. “Reshuffling scale-free networks: from random to assortative”. Phys. Rev. E 70 (2004), 066102.
[318] S., Yook et al. “Weighted evolving networks”. Phys. Rev. Lett. 86 (2001), 5835–5838.
[319] G. U., Yule. “A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S.” Phylos. T. R. Soc. B (1924).
[320] W.W., Zachary. “An information flow model for conflict and fission in small groups”. J. Anthropol. Res. 33 (1977), 452–473.
[321] D., Zheng et al. “Weighted scale-free networks with stochastic weight assignments”. Phys. Rev. E 67 (2003), 040102.
[322] C., Zhou and J., Kurths. “Dynamical weights and enhanced synchronization in adaptive complex networks”. Phys. Rev. Lett. 96 (2006), 164102.
[323] S., Zhou and R. J., Mondragón. “The rich-club phenomenon in the Internet topology”. IEEE Commun. Lett. 8 (2004), 180–182.
[324] T., Zhou et al. “Bipartite network projection and personal recommendation”. Phys. Rev. E 76 (2007), 046115.
[325] G.K., Zipf. Human Behavior and the Principle of Least Effort. Addison-Wesley, Reading, MA (USA), 1949.