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Information diffusion in distributed OSN: The impact of trusted relationships

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Abstract

Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to “active social contacts” leads to a graph with high network connectivity, where the nodes are still well-connected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.

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Notes

  1. http://current.cs.ucsb.edu/socialnets/

References

  1. Diaspora Project - https://diasporafoundation.org/

  2. Adali S, Escriva R, Goldberg MK, Hayvanovych M, Magdon-Ismail M, Szymanski BK, Wallace WA, Williams G (2010) Measuring behavioral trust in social networks. In: ISI ’10, pp 150–152. doi:10.1109/ISI.2010.5484757

  3. Arnaboldi V, Conti M, Passarella A, Pezzoni F (2012) Analysis of ego network structure in online social networks. In: SocialCom ’12, pp 31–40

  4. Arnaboldi V, Guazzini A, Passarella A (2013) Egocentric online social networks: analysis of key features and prediction of tie strength in facebook. Comput Commun 36(10–11):1130–1144. doi:10.1016/j.comcom.2013.03.003

    Article  Google Scholar 

  5. Arnaboldi V, La Gala ML, Passarella A, Conti M (2014) The role of trusted relationships on content spread in distributed online social networks. In: LSDVE: LSDVE ’14, pp 287–298

  6. Bakshy E, Hofman JM, Watts DJ, Mason WA (2011) Everyone’s an influencer: quantifying influence on twitter. In: WSDM ’11, pp 65–74

  7. Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: WWW ’12, pp 519–528

  8. Bollobás B (1984) The evolution of random graphs. Trans Am Math Soc 286(1):257. doi:10.2307/1999405

    Article  MathSciNet  MATH  Google Scholar 

  9. Borgia E (2014) The internet of things vision: key features, applications and open issues. Comput Commun 54:1–31. doi:10.1016/j.comcom.2014.09.008

    Article  Google Scholar 

  10. Buchegger S (2009) Delay-tolerant social networking. In: Extreme workshop on communication, pp 1–2

  11. Buchegger S, Schioberg D, Vu LH, Datta A (2009) PeerSoN: P2P social networking - early experiences and insights. In: SocialNets ’09, pp 46–52

  12. Cha M, Benevenuto F (2012) The world of connections and information flow in twitter. Trans Syst Man Cybern 42(4):991–998

    Article  Google Scholar 

  13. Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. WWW, p 721. doi:10.1145/1526709.1526806

  14. Cheng J, Adamic L, Dow P, Kleinberg J, Leskovec J (2014) Can cascades be predicted?. In: WWW ’14

  15. Conti M, Das S, Bisdikian C, Kumar M, Ni LM, Passarella A, Roussos G, Tröster G, Tsudik G, Zambonelli F (2012) Looking ahead in pervasive computing: challenges and opportunities in the era of cyber-physical convergence. Pervasive Mob Comput 8(1):2–21. doi:10.1016/j.pmcj.2011.10.001

    Article  Google Scholar 

  16. Cutillo LA, Molva R, Strufe T (2009) Safebook: a privacy-preserving online social network leveraging on real-life trust. IEEE Commun Mag 47(12):94–101

    Article  Google Scholar 

  17. Galuba W, Aberer K, Chakraborty D, Despotovic Z, Kellerer W (2010) Outtweeting the twitterers - predicting information cascades in microblogs. In: WOSN ’10, pp 3–3

  18. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223. doi:10.1023/a:1011122126881

    Article  Google Scholar 

  19. Guidi B, Conti M, Ricci L (2013) P2P architectures for distributed online social networks. In: International conference on high performance computing & simulation (HPCS), pp 678–681. doi:10.1109/HPCSim.2013.6641493

  20. Guille A, Hacid H, Favre C, Zighed D (2013) Information diffusion in online social networks: a survey. ACM SIGMOD Rec 42(2):17–28. doi:10.1145/2503792.2503797

    Article  Google Scholar 

  21. Han L, Nath B, Iftode L, Muthukrishnan S (2011) Social butterfly: social caches for distributed social networks. In: SocialCom ’11, pp 81–86. doi:10.1109/PASSAT/SocialCom.2011.105

  22. Hill RA, Dunbar RI (2003) Social network size in humans. Hum Nat 14(1):53–72. doi:10.1007/s12110-003-1016-y

    Article  Google Scholar 

  23. Hill S, Provost F, Volinsky C (2006) Network-based marketing: identifying likely adopters via consumer networks. Stat Sci 21(2):256–276. doi:10.1214/088342306000000222

    Article  MathSciNet  MATH  Google Scholar 

  24. Kleinberg J (2000) The small-world phenomenon: an algorithmic perspective*. In: STOC ’00, pp 163–170

  25. Lanier J (2013) Who owns the future?

  26. Newman ME, Park J (2003) Why social networks are different from other types of networks. Phys Rev E 68(3). doi:10.1103/PhysRevE.68.036122

  27. Paul T, Famulari A, Strufe T (2014) A survey on decentralized online social networks. Comput Netw 75:437–452. doi:10.1016/j.comnet.2014.10.005

    Article  Google Scholar 

  28. Petrovic S, Osborne M, Lavrenko V (2011) RT to win! Predicting message propagation in twitter. In: ICWSM

  29. Schulz A, Ristoski P, Paulheim H (2013) I see a car crash: real-time detection of small scale incidents in microblogs. In: ESWC ’13, vol 7955, pp 22–23

  30. Sermpezis P, Spyropoulos T (2014) Understanding the effects of social selfishness on the performance of heterogeneous opportunistic networks. Comput Commun 48:71–83. doi:10.1016/j.comcom.2014.03.016

    Article  Google Scholar 

  31. Shmidt CW (2012) Using social media to predict and track deseases outbreaks. Environ Health Perspect 120(1):30–33. doi:10.1289/ehp.120-a30

    Article  Google Scholar 

  32. Sun E, Rosenn I, Marlow C, Lento T (2009) Gesundheit! Modeling contagion through facebook news feed. In: ICWSM 2000

  33. Sutcliffe A (2012) Social relationships and the emergence of social networks. J Artif Soc Soc Simul 15(4):1–19

  34. Sutcliffe A, Dunbar RI, Binder J, Arrow H (2012) Relationships and the social brain: integrating psychological and evolutionary perspectives. Br J Psychol 103(2):149–68. doi:10.1111/j.2044-8295.2011.02061.x

    Article  Google Scholar 

  35. Taxidou I, Fischer PM (2014) Online analysis of information diffusion in twitter. In: WWW Companion ’14, pp 1313–1318

  36. Taxidou I, Fischer PM (2014) RApID: a system for real-time analysis of information diffusion in twitter. In: CIKM ’14, pp 2060–2062

  37. Travers J, Milgram S (1969) An experimental study of the small world problem. Sociometry 32(4):425. doi:10.2307/2786545

    Article  Google Scholar 

  38. Wilson C, Sala A, Puttaswamy KP, Zhao BY (2012) Beyond social graphs: user interactions in online social networks and their implications. ACM Trans Web 6(4):1–31. doi:10.1145/2382616.2382620

    Article  Google Scholar 

  39. Zhu K, Li W, Fu X (2014) Rethinking routing information in mobile social networks: location-based or social-based? Comput Commun 42:24–37. doi:10.1016/j.comcom.2014.01.008

    Article  Google Scholar 

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Acknowledgments

This work was partly funded by the EC under the EINS (FP7-FIRE 288021), MOTO (FP7 317959), and EIT ICT Labs MOSES and oT content (Business Plan 2015) projects.

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Authors and Affiliations

  1. IIT-CNR, Via G. Moruzzi 1, 56017, Pisa, Italy

    Valerio Arnaboldi, Massimiliano La Gala, Andrea Passarella & Marco Conti

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  1. Valerio Arnaboldi
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  2. Massimiliano La Gala
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  3. Andrea Passarella
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  4. Marco Conti
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Correspondence to Valerio Arnaboldi.

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Arnaboldi, V., La Gala, M., Passarella, A. et al. Information diffusion in distributed OSN: The impact of trusted relationships. Peer-to-Peer Netw. Appl. 9, 1195–1208 (2016). https://doi.org/10.1007/s12083-015-0395-2

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