Knowledge

Link-centric preferential attachment

Source 📝

215:
spread of the contagion to the entire population. Compared to the classic rumour spreading process where nodes are memory-less, link-centric preferential attachment can cause not only a slower spread of the contagion but also one less diffuse. In these models an infected node's chances of connecting to new contacts diminishes as their size of their social circle
214:
Understanding the evolution of a network's structure and how it can influence dynamical processes has become an important part of modeling the spreading of contagions. In models of social and biological contagion spreading on time-varying networks link-centric preferential attachment can alter the
40:
examined this behaviour in the social networks of a group of workers and identified tie strength, a characteristic of social ties describing the frequency of contact between two individuals. From this comes the idea of strong and weak ties, where an individual's strong ties are those she has come
41:
into frequent contact with. Link-centric preferential attachment aims to explain the mechanism behind strong and weak ties as a stochastic reinforcement process for old ties in agent-based modeling where nodes have long-term memory.
190: 121: 235:
grows leading to a limiting effect on the growth of n. The result is strong ties with a node's early contacts and consequently the weakening of the diffusion of the contagion.
206:
More complex models may take into account other variables, such as frequency of contact, contact and intercontact duration, as well as short term memory effects.
233: 67: 36:
In real social networks individuals exhibit a tendency to re-connect with past contacts (ex. family, friends, co-workers, etc.) rather than strangers. In 1970,
69:, the number of contacts it has had in the past. The probability for a node with n social ties to establish a new social tie could then be simply given by 203:(2) = 1/(2 + 1) = 1/3 to create a new tie with D, whereas the probability to reconnect with A or C is twice that at 2/3. 281:
Vestergaard, Christian L.; Genois, Mathieu; Barrat, Alain (October 9, 2014). "How memory generates hetergeneous dynamics in temporal networks".
195:
Figure 1. shows an example of this process: in the first step nodes A and C connect to node B, giving B a total of two social ties. With
708: 49:
In a simple model for this mechanism, a node's propensity to establish a new link can be characterized solely by
703: 25: 136: 75: 651: 596: 498: 426: 361: 342:
Barabasi, Albert-Laszlo; Albert, Reka (October 15, 1999). "Emergence of Scaling in Random Networks".
300: 21: 403:
Perra, Nicola; Goncalves, Bruno; Pastor-Satorras, Romualdo; Vespignani, Alessandro (June 25, 2012).
620: 586: 559: 488: 416: 385: 351: 324: 290: 20:
is a node's propensity to re-establish links to nodes it has previously been in contact with in
679: 612: 524: 452: 377: 316: 254: 669: 659: 604: 551: 514: 506: 442: 434: 369: 308: 37: 249: 639: 655: 600: 502: 430: 365: 304: 674: 519: 476: 447: 404: 218: 130:
is an offset constant. The probability for a node to re-connect with old ties is then
52: 697: 624: 563: 328: 28:
model relies on nodes keeping memory of previous neighbors up to the current time.
389: 664: 373: 638:
Kamp, Christel; Moslonka-Lefebvre, Mathieu; Alizon, Samuel (December 13, 2013).
608: 312: 577:
Newman, M. E. J. (July 26, 2002). "Spread of epidemic disease on networks".
683: 616: 528: 456: 381: 320: 591: 356: 244: 510: 438: 555: 493: 421: 295: 210:
Effects on the spreading of contagions / weakness of strong ties
542:
Granovetter, Mark (1973). "The strength of weak ties".
477:"Time-varying networks and the weakness of strong ties" 221: 199: = 1, in the next step B has a probability 139: 78: 55: 405:"Activity driven modeling of time-varying networks" 227: 184: 115: 61: 470: 468: 466: 276: 274: 272: 270: 16:In mathematical modeling of social networks, 8: 673: 663: 590: 518: 492: 446: 420: 355: 294: 220: 161: 138: 112: 94: 77: 54: 266: 640:"Epidemic Spread on Weighted Networks" 185:{\displaystyle 1-P(n)={n \over n+c}.} 7: 475:Karsai, Marton (February 10, 2014). 116:{\displaystyle P(n)={c \over n+c}\,} 18:link-centric preferential attachment 14: 155: 149: 88: 82: 1: 544:American Journal of Sociology 665:10.1371/journal.pcbi.1003352 374:10.1126/science.286.5439.509 725: 644:PLOS Computational Biology 609:10.1103/PhysRevE.66.016128 313:10.1103/PhysRevE.90.042805 26:preferential attachment 229: 186: 117: 63: 230: 187: 118: 64: 22:time-varying networks 709:Stochastic processes 219: 137: 76: 53: 656:2013PLSCB...9E3352K 601:2002PhRvE..66a6128N 503:2014NatSR...4E4001K 431:2012NatSR...2E.469P 366:1999Sci...286..509B 305:2014PhRvE..90d2805V 650:(1371): e1003352. 481:Scientific Reports 409:Scientific Reports 225: 182: 113: 59: 585:(16128): 016128. 579:Physical Review E 511:10.1038/srep04001 439:10.1038/srep00469 350:(5439): 509–512. 283:Physical Review E 255:Interpersonal tie 228:{\displaystyle n} 177: 110: 62:{\displaystyle n} 716: 688: 687: 677: 667: 635: 629: 628: 594: 592:cond-mat/0205009 574: 568: 567: 550:(6): 1360–1380. 539: 533: 532: 522: 496: 472: 461: 460: 450: 424: 400: 394: 393: 359: 357:cond-mat/9910332 339: 333: 332: 298: 278: 234: 232: 231: 226: 191: 189: 188: 183: 178: 176: 162: 122: 120: 119: 114: 111: 109: 95: 68: 66: 65: 60: 38:Mark Granovetter 724: 723: 719: 718: 717: 715: 714: 713: 694: 693: 692: 691: 637: 636: 632: 576: 575: 571: 541: 540: 536: 474: 473: 464: 402: 401: 397: 341: 340: 336: 280: 279: 268: 263: 250:Network science 241: 217: 216: 166: 135: 134: 99: 74: 73: 51: 50: 47: 34: 12: 11: 5: 722: 720: 712: 711: 706: 704:Network theory 696: 695: 690: 689: 630: 569: 556:10.1086/225469 534: 462: 395: 334: 265: 264: 262: 259: 258: 257: 252: 247: 240: 237: 224: 193: 192: 181: 175: 172: 169: 165: 160: 157: 154: 151: 148: 145: 142: 124: 123: 108: 105: 102: 98: 93: 90: 87: 84: 81: 58: 46: 43: 33: 30: 13: 10: 9: 6: 4: 3: 2: 721: 710: 707: 705: 702: 701: 699: 685: 681: 676: 671: 666: 661: 657: 653: 649: 645: 641: 634: 631: 626: 622: 618: 614: 610: 606: 602: 598: 593: 588: 584: 580: 573: 570: 565: 561: 557: 553: 549: 545: 538: 535: 530: 526: 521: 516: 512: 508: 504: 500: 495: 490: 486: 482: 478: 471: 469: 467: 463: 458: 454: 449: 444: 440: 436: 432: 428: 423: 418: 414: 410: 406: 399: 396: 391: 387: 383: 379: 375: 371: 367: 363: 358: 353: 349: 345: 338: 335: 330: 326: 322: 318: 314: 310: 306: 302: 297: 292: 289:(4): 042805. 288: 284: 277: 275: 273: 271: 267: 260: 256: 253: 251: 248: 246: 243: 242: 238: 236: 222: 212: 211: 207: 204: 202: 198: 179: 173: 170: 167: 163: 158: 152: 146: 143: 140: 133: 132: 131: 129: 106: 103: 100: 96: 91: 85: 79: 72: 71: 70: 56: 44: 42: 39: 31: 29: 27: 23: 19: 647: 643: 633: 582: 578: 572: 547: 543: 537: 484: 480: 412: 408: 398: 347: 343: 337: 286: 282: 213: 209: 208: 205: 200: 196: 194: 127: 125: 48: 35: 17: 15: 698:Categories 261:References 32:Background 494:1303.5966 422:1203.5351 296:1409.1805 144:− 684:24348225 625:15291065 617:12241447 564:59578641 529:24510159 487:: 4001. 457:22741058 382:10521342 329:16022001 321:25375547 245:BA model 239:See also 45:Examples 675:3861041 652:Bibcode 597:Bibcode 520:3918922 499:Bibcode 448:3384079 427:Bibcode 415:: 469. 362:Bibcode 344:Science 301:Bibcode 24:. This 682:  672:  623:  615:  562:  527:  517:  455:  445:  390:524106 388:  380:  327:  319:  126:where 621:S2CID 587:arXiv 560:S2CID 489:arXiv 417:arXiv 386:S2CID 352:arXiv 325:S2CID 291:arXiv 680:PMID 613:PMID 525:PMID 453:PMID 378:PMID 317:PMID 670:PMC 660:doi 605:doi 552:doi 515:PMC 507:doi 443:PMC 435:doi 370:doi 348:286 309:doi 700:: 678:. 668:. 658:. 646:. 642:. 619:. 611:. 603:. 595:. 583:66 581:. 558:. 548:78 546:. 523:. 513:. 505:. 497:. 483:. 479:. 465:^ 451:. 441:. 433:. 425:. 411:. 407:. 384:. 376:. 368:. 360:. 346:. 323:. 315:. 307:. 299:. 287:90 285:. 269:^ 686:. 662:: 654:: 648:9 627:. 607:: 599:: 589:: 566:. 554:: 531:. 509:: 501:: 491:: 485:4 459:. 437:: 429:: 419:: 413:2 392:. 372:: 364:: 354:: 331:. 311:: 303:: 293:: 223:n 201:P 197:c 180:. 174:c 171:+ 168:n 164:n 159:= 156:) 153:n 150:( 147:P 141:1 128:c 107:c 104:+ 101:n 97:c 92:= 89:) 86:n 83:( 80:P 57:n

Index

time-varying networks
preferential attachment
Mark Granovetter
BA model
Network science
Interpersonal tie




arXiv
1409.1805
Bibcode
2014PhRvE..90d2805V
doi
10.1103/PhysRevE.90.042805
PMID
25375547
S2CID
16022001
arXiv
cond-mat/9910332
Bibcode
1999Sci...286..509B
doi
10.1126/science.286.5439.509
PMID
10521342
S2CID
524106

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.