LEADER 01436nam0 22003253i 450 001 VAN0250449 005 20240202125426.591 017 70$2N$a9780429503559 100 $a20220920d1995 |0itac50 ba 101 $aeng 102 $aUS 105 $a|||| ||||| 200 1 $aˆAn ‰introduction to quantum field theory$fMichael E. Peskin, Daniel V. Schroeder 210 $aBoca Raton$cCRC$d1995 215 $axxii, 842 p.$cill.$d24 cm 500 1$3VAN0250448$aˆAn ‰introduction to quantum field theory$91762824 606 $a81-XX$xQuantum theory [MSC 2020]$3VANC019967$2MF 606 $a81Txx$xQuantum field theory; related classical field theories [MSC 2020]$3VANC027580$2MF 620 $aUS$dBoca Raton$3VANL000070 700 1$aPeskin$bMicahel E.$3VANV106017$0789925 701 1$aSchroeder$bDaniel V.$3VANV106018$053977 712 $aCRC $3VANV108066$4650 801 $aIT$bSOL$c20240209$gRICA 856 4 $uhttp://doi.org/10.1201/9780429503559$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN0250449 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08CONS e-book $e08DMF644 20220920 996 $aIntroduction to quantum field theory$91762824 997 $aUNICAMPANIA LEADER 05469nam 2200721Ia 450 001 9911018914003321 005 20200520144314.0 010 $a9786610520992 010 $a9781280520990 010 $a128052099X 010 $a9783527606337 010 $a3527606335 010 $a9783527602759 010 $a3527602755 035 $a(CKB)1000000000016780 035 $a(EBL)481793 035 $a(OCoLC)69176514 035 $a(SSID)ssj0000167634 035 $a(PQKBManifestationID)11171509 035 $a(PQKBTitleCode)TC0000167634 035 $a(PQKBWorkID)10178995 035 $a(PQKB)11469897 035 $a(MiAaPQ)EBC481793 035 $a(Perlego)2768838 035 $a(EXLCZ)991000000000016780 100 $a20021223d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aHandbook of graphs and networks $efrom the Genome to the Internet /$fStefan Bornholdt, Heinz Georg Schuster (eds.) 205 $a1st ed. 210 $aWeinheim ;$a[Cambridge] $cWiley-VCH$dc2003 215 $a1 online resource (419 p.) 300 $aDescription based upon print version of record. 311 08$a9783527403363 311 08$a3527403361 320 $aIncludes bibliographical references and index. 327 $aHandbook of Graphs and Networks From the Genome to the Internet; Preface; Contents; List of contributors; 1 Mathematical results on scale-free random graphs; 1.1 Introduction; 1.2 Classical models of random graphs; 1.3 Results for classical random graphs; 1.4 The Watts-Strogatz 'small-world' model; 1.5 Scale-free models; 1.6 The Baraba?si-Albert model; 1.7 The LCD model and G((n))(m); 1.8 The Buckley-Osthus model; 1.9 The copying model; 1.10 The Cooper-Frieze model; 1.11 Directed scale-free graphs; 1.12 Clustering coefficient and small subgraphs 327 $a1.13 Pairings on [0, 1] and the diameter of the LCD model1.14 Robustness and vulnerability; 1.15 The case [0, 1]: plane-oriented recursive trees; 1.16 Conclusion; References; 2 Random graphs as models of networks; 2.1 Introduction; 2.2 Random graphs with specified degree distributions; 2.3 Probability generating functions; 2.3.1 Properties of generating functions; 2.3.2 Examples; 2.4 Properties of undirected graphs; 2.4.1 Distribution of component sizes; 2.4.2 Mean component size; 2.4.3 Above the phase transition; 2.5 Properties of directed graphs; 2.5.1 Generating functions; 2.5.2 Results 327 $a2.6 Networks with clustering2.7 Models defined on random graphs; 2.7.1 Network resilience; 2.7.2 Epidemiology; 2.7.3 The SIR model; 2.7.4 Solution of the SIR model; 2.8 Summary; References; 3 Emergence of scaling in complex networks; 3.1 Introduction; 3.2 Network models; 3.2.1 Random networks; 3.2.2 Scale-free networks; 3.2.3 Scale-free model; 3.3 Fitness model and Bose-Einstein condensation; 3.4 The Achilles' Heel of complex networks; 3.5 A deterministic scale-free model; 3.6 Outlook; 3.7 Acknowledgments; References; 4 Structural properties of scale-free networks; 4.1 Introduction 327 $a4.1.1 Random graphs4.1.2 Scale-free networks; 4.2 Small and Ultra-small worlds; 4.2.1 Diameter of scale-free networks; 4.2.2 Minimal graphs and lower bound; 4.2.3 The general case of random scale-free networks; 4.3 Percolation; 4.3.1 Random breakdown; 4.3.2 Percolation critical threshold; 4.3.3 Generating functions; 4.3.4 Intentional attack; 4.3.5 Critical exponents; 4.3.6 Fractal dimension; 4.4 Percolation in directed networks; 4.4.1 Threshold; 4.4.2 Critical exponents; 4.5 Efficient immunization strategies; 4.5.1 Acquaintance immunization; 4.6 Summary and outlook; References 327 $a5 Epidemics and immunization in scale-free networks5.1 Introduction; 5.2 Computers and epidemiology; 5.3 Epidemic spreading in homogeneous networks; 5.4 Real data analysis; 5.5 Epidemic spreading in scale-free networks; 5.5.1 Analytic solution for the Baraba?si-Albert network; 5.5.2 Finite size scale-free networks; 5.6 Immunization of scale-free networks; 5.6.1 Uniform immunization; 5.6.2 Targeted immunization; 5.7 Conclusions; References; 6 Cells and genes as networks in nematode development and evolution; 6.1 Introduction 327 $a6.2 Nematode developmental biology: studying processes at a cellular level 330 $aComplex interacting networks are observed in systems from such diverse areas as physics, biology, economics, ecology, and computer science. For example, economic or social interactions often organize themselves in complex network structures. Similar phenomena are observed in traffic flow and in communication networks as the internet. In current problems of the Biosciences, prominent examples are protein networks in the living cell, as well as molecular networks in the genome. On larger scales one finds networks of cells as in neural networks, up to the scale of organisms in ecological food web 606 $aSystem analysis 606 $aGraph theory 606 $aCombinatorial analysis 615 0$aSystem analysis. 615 0$aGraph theory. 615 0$aCombinatorial analysis. 676 $a003 676 $a511.5 701 $aBornholdt$b Stefan$0737704 701 $aSchuster$b Heinz Georg$f1943-$048625 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911018914003321 996 $aHandbook of graphs and networks$91460732 997 $aUNINA