Information diffusion in signed networks
Autoři:
Xiaochen He aff001; Haifeng Du aff001; Marcus W. Feldman aff001; Guangyu Li aff001
Působiště autorů:
Center for Administration and Complexity Science, Xi’an Jiaotong University, Xi’an, China
aff001; Department of Sociology, Cornell University, Ithaca, New York, United States of America
aff002; Department of Biology, Stanford University, Stanford, California, United States of America
aff003
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0224177
Souhrn
Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of information based on balance theory, and find that a signed network can generate path dependence while structural balance can help remove the path dependence when seeded with balanced initialized active nodes. Simulation shows that the diffusion of information based on positive links contradicts that based on negative links. More positive links in signed networks are more likely to activate nodes and remove path dependence, but they can reduce predictability that is based on active states. We also find that a balanced structure can facilitate both the magnitude and speed of information diffusion, remove the path dependence, and cause polarization.
Klíčová slova:
Centrality – Collective human behavior – Computer and information sciences – Evolutionary theory – Network analysis – Simulation and modeling – Social networks – Sociology
Zdroje
1. Dodson JA, Muller E. Models of new product diffusion through advertising and word-of-mouth. Manage. Sci. 1978; 24(15): 1568–1578.
2. Coleman J, Katz E, Menzel H. The diffusion of an innovation among physicians. Sociometry 1957; 20(4): 253–270.
3. Bikhchandani S, Hirshleifer D, Welch I. A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 1992; 100(5): 992–1026.
4. Valente TW. Network models of the diffusion of innovations. Comput. Math. O. Th. 1996; 2(2): 163–164.
5. Chierichetti F, Lattanzi S, Panconesi A. Rumour spreading and graph conductance. Proc. Annu. ACM SIAM Symp. Discret. Algorithms 2010; 1657–1663.
6. Goldenberg J, Libai B, Muller E. Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. Market. Lett. 2001; 12(3): 211–223.
7. Granovetter M. Threshold Models of Collective Behavior. AM. J. Sociol. 1978; 83(6):1420–1443.
8. Kempe D, Kleinberg J, Éva Tardos. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 2003; 137–146.
9. Chen W, Collins A, Cummings R, et al. Influence maximization in social networks when negative opinions may emerge and propagate. Proc. SIAM Int. Conf. Data Min. 2011; 379–390.
10. Borodin A, Filmus Y, Oren J. Threshold Models for Competitive Influence in Social Networks. Int. Workshop Internet Network Econ. Springer, Berlin, Heidelberg 2010; 539–550.
11. Lee W, Kim J, Yu H. CT-IC: Continuously Activated and Time-Restricted Independent Cascade Model for Viral Marketing. IEEE Int. Conf. Data Min. 2013; 57–68.
12. Wang Y, Wang H, Li J, et al. Efficient influence maximization in weighted independent cascade model. Int. Conf. Database Syst. Adv. Appl. Springer, Cham 2016; 49–64.
13. Moreno Y, Pastor-Satorras R, Vespignani A. Epidemic outbreaks in complex heterogeneous networks. Eur. Phys. J. B 2002; 26(4): 521–529.
14. May RM, Lloyd AL. Infection dynamics on scale-free networks. Phys. Rev. E 2001; 64(2): 066112.
15. Pastorsatorras R, Vespignani A. Epidemic spreading in scale-free networks. Phys. Rev. Lett. 2000; 86(14): 3200–3203.
16. Zhou X, Cui J. Analysis of stability and bifurcation for a SEIR epidemic model with saturated recovery rate. Commun Nonliner Sci. 2011; 16(11):4438–4450.
17. Alvarez-Hamelin JI, Fleury E, Vespignani A, Ziviani A. Complex dynamic networks: Tools and methods (Guest Editorial). Comput. Networks. 2012; 56(3):967–969.
18. Snijders TAB, Steglich CEG, Schweinberger M. Modeling the co-evolution of networks and behavior. In Longitudinal Models in the Behavioral and Related Sciences (eds van Montfort K, Oud H, Satorra A). Mahwah: Erlbaum; 2007; 41–71.
19. Steglich CEG, Snijders TAB, Pearson M. Dynamic networks and behavior: separating selection from influence. Sociol. Methodol. 2010; 40: 329–393.
20. Aral S, Muchnik L, Sundararajan A. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. USA. 2009; 106(51): 21544–21549. doi: 10.1073/pnas.0908800106 20007780
21. Greenan CC. Diffusion of innovations in dynamic networks. J. R. Stat. Soc., Ser. A: Stat. Soc. 2015; 178(1): 147–166.
22. Apolloni A, Channakeshava K, Durbeck L, et al. A study of information diffusion over a realistic social network model. Int. Conf. Comput. Sci. Eng. IEEE 2009; 4: 675–682.
23. Gayraud N T H, Pitoura E, Tsaparas P. Diffusion maximization in evolving social networks. Proc. ACM Conf. Online Soc. Networks 2015; 125–135.
24. Guimarães A, Vieira AB, Silva APC, Ziviani A. Fast centrality-driven diffusion in dynamic networks. Proc. 22nd Int. Conf. World Wide Web. ACM 2013; 821–828.
25. Santos FC, Pacheco JM, Lenaerts T. Cooperation prevails when individuals adjust their social ties. Plos Comput Biol. 2006; 2 (10): 1284–1291.
26. Pinheiro FL, Santos FC, Pacheco JM. Linking Individual and Collective Behavior in Adaptive Social Networks. Phys Rev Lett. 2016; 116 (12): 128702. doi: 10.1103/PhysRevLett.116.128702 27058108
27. He X, Du H, Cai M, Feldman MW. The evolution of cooperation in signed networks under the impact of structural balance. Plos One. 2018; 13(10): e0205084. doi: 10.1371/journal.pone.0205084 30296278
28. Easley D, Kleinberg J. Networks, crowds, and markets. Cambridge: Cambridge university press; 2010.
29. Mark NP. Culture and Competition: Homophily and Distancing Explanations for Cultural Niches. Am. Sociol. Rev. 2003; 68(3): 319–345.
30. Macy MW, Kitts J A, Flache A, et al. Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure. Dyn. Soc. Network Model. Anal. 2003; 162–173.
31. Jager W. Uniformity, Bipolarization and Pluriformity Captured as Generic Stylized Behavior with an Agent-Based Simulation Model of Attitude Change. Comput. Math. Organ. Th. 2005; 10(4):295–303.
32. Kitts JA. Social influence and the emergence of norms amid ties of amity and enmity. Simul. Model. Pract. Th. 2006; 14(4): 407–422.
33. Doreian P, Mrvar A. Partitioning signed social networks. Soc. Networks 2009; 31(1): 1–11.
34. Heider F. Social perception and phenomenal causality. Psychol. Rev. 1944; 51(6): 358–374.
35. McPherson M, Smith-Lovin L, Cook JM. Birds of a feather Homophily in social networks. Annu. Rev. Sociol. 2001; 27(1): 415–444.
36. Rogers EM, Bhowmik DK. Homophily-heterophily: Relational concepts for communication research[J]. Public Opin. Quart. 1970; 34 (4): 523–538.
37. Kandel DB. Homophily, Selection, and Socialization in Adolescent Friendships. Am. J. Sociol. 1978; 84(2): 427–436.
38. Kossinets G, Watts DJ. Origins of Homophily in an Evolving Social Network. Am. J. Sociol. 2010, 115(2): 405–450.
39. Tajfel H. Differentiation between social groups: Studies in the social psychology of intergroup relations [J]. Am. J. Sociol. 1978, 86(5).
40. Tajfel H, Turner J. An integrative theory of intergroup conflict. Soc. Psychol. Intergroup Relat. 1979; 33: 94–109.
41. Mummendey A, Kessler T, Klink A, et al. Strategies to cope with negative social identity: Predictions by social identity theory and relative deprivation theory. J. Pers. Soc. Psychol. 1999; 76(2): 229–245. doi: 10.1037//0022-3514.76.2.229 10074707
42. Terry DJ, Hogg MA. Group Norms and the Attitude-Behavior Relationship: A Role for Group Identification. Pers. Soc. Psychol. B. 1996; 22(8): 776–793.
43. Centola D, Eguíluz VM, Macy MW. Cascade dynamics of complex propagation. Physica A 2007; 374(1): 449–456.
44. Watts DJ, Strogatz SH. Collective dynamics of small-world networks. Nature 1998; 440–442. doi: 10.1038/30918 9623998
45. Watts DJ. A Simple Model of Global Cascades on Random Networks. Proc. Natl. Acad. Sci. USA. 2002; 99(9):5766–71. doi: 10.1073/pnas.082090499 16578874
46. Morris S. Contagion. Rev. Econ. Stud. 2010; 67(1): 57–78.
47. Heider F. Attitudes and cognitive organization. J. Psychol. 1946; 21(1): 107.
48. Cartwright D, Harary F. Structural balance: a generalization of Heider’s theory. Psychol. Rev. 1956; 63(5): 277–292. doi: 10.1037/h0046049 13359597
49. Du H, He X, Feldman MW. Structural balance in fully signed networks. Complexity 2016; 21(S1): 497–511.
50. Du H, He X, Wang S, et al. Optimizing transformations of structural balance in signed networks with potential relationships. Physica A 2017; 465: 414–424.
51. Salganik MJ. Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science 2006; 311(5762): 854–856. doi: 10.1126/science.1121066 16469928
52. Girvan M, Newman MEJ. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA. 2002; 99(12): 7821–7826. doi: 10.1073/pnas.122653799 12060727
53. Coser LA. The functions of social conflict. Am. Sociol. Rev. 1956; 22(1): 112.
54. Tajfel H, Billig MG, Bundy RP. Social categorization and intergroup behavior. Eur. J. Soc. Psychol. 1971; 1(2): 149–178.
55. Walker I, Smith HJ. Relative deprivation: Specification, development, and integration. Cambridge: Cambridge University Press; 2002.
56. Du H, He X, Wang J, et al. Reversing structural balance in signed networks. Physica A 2018; 503: 780–792.
Článek vyšel v časopise
PLOS One
2019 Číslo 10
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Correction: Low dose naltrexone: Effects on medication in rheumatoid and seropositive arthritis. A nationwide register-based controlled quasi-experimental before-after study
- Combining CDK4/6 inhibitors ribociclib and palbociclib with cytotoxic agents does not enhance cytotoxicity
- Experimentally validated simulation of coronary stents considering different dogboning ratios and asymmetric stent positioning
- Risk factors associated with IgA vasculitis with nephritis (Henoch–Schönlein purpura nephritis) progressing to unfavorable outcomes: A meta-analysis
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy