research-article

Spreading rumours without the network

Authors:

Alessandro Epasto,

Alessandro Panconesi,

Piotr SankowskiAuthors Info & Claims

COSN '14: Proceedings of the second ACM conference on Online social networks

Pages 107 - 118

https://doi.org/10.1145/2660460.2660472

Published: 01 October 2014 Publication History

Abstract

In this paper we tackle the following question: is it possible to predict the characteristics of the evolution of an epidemic process in a social network on the basis of the degree distribution alone? We answer this question affirmatively for several diffusion processes-- Push-Pull, Broadcast and SIR-- by showing that it is possible to predict with good accuracy their average evolution. We do this by developing a space efficient predictor that makes it possible to handle very large networks with very limited computational resources. Our experiments show that the prediction is surprisingly good for many instances of real-world networks. The class of real-world networks for which this happens can be characterized in terms of their neighbourhood function, which turns out to be similar to that of random networks. Finally, we analyse real instances of rumour spreading in Twitter and observe that our model describes qualitatively well their evolution.

References

[1]

R. Albert, H. Jeong, and A. L. Barabasi. The diameter of the world wide web. Nature, 1999.

[2]

R. Andersen, F. Chung, and K. Lang. Local graph partitioning using pagerank vectors. In FOCS, 2006.

Digital Library

[3]

L. Backstrom, P. Boldi, M. Rosa, J. Ugander, and S. Vigna. Four degrees of separation. In WebSci, 2012.

Digital Library

[4]

M. Bayati, J. Kim, and A. Saberi. A sequential algorithm for generating random graphs. Algorithmica, 2010.

Digital Library

[5]

M. Boguá, R. Pastor-Satorras, and A. Vespignani. Epidemic spreading in complex networks with degree correlations. Statistical mechanics of complex networks, 2003.

[6]

P. Boldi, M. Rosa, and S. Vigna. HyperANF: Approximating the neighbourhood function of very large graphs on a budget. In WWW, 2011.

Digital Library

[7]

P. Boldi and S. Vigna. The WebGraph framework I: Compression techniques. In WWW, 2004.

Digital Library

[8]

S. Chatterjee, R. Durrett, et al. Contact processes on random graphs with power law degree distributions have critical value 0. The Annals of Probability, 2009.

[9]

F. Chierichetti, R. Kumar, S. Lattanzi, M. Mitzenmacher, A. Panconesi, and P. Raghavan. On compressing social networks. In KDD, 2009.

Digital Library

[10]

F. Chierichetti, S. Lattanzi, and A. Panconesi. Almost tight bounds for rumour spreading with conductance. In Proc. 42nd STOC, 2010.

Digital Library

[11]

F. Chierichetti, S. Lattanzi, and A. Panconesi. Rumour spreading and graph conductance. In SODA, 2010.

Digital Library

[12]

A. Demers, D. Greene, C. Hauser, W. Irish, J. Larson, S. Shenker, H. Sturgis, D. Swinehart, and D. Terry. Epidemic algorithms for replicated database maintenance. In PODC, 1987.

Digital Library

[13]

U. Feige, D. Peleg, P. Raghavan, and E. Upfal. Randomized broadcast in networks. In Algorithms. Springer, 1990.

Digital Library

[14]

N. Fountoulakis, K. Panagiotou, and T. Sauerwald. Ultra-fast rumor spreading in social networks. In SODA, 2012.

Digital Library

[15]

G. Giakkoupis. Tight bounds for rumor spreading in graphs of a given conductance. In T. Schwentick and C. Dürr, editors, STACS, 2011.

[16]

S. Goel, A. Anderson, J. Hofman, and D. Watts. The structural virality of online diffusion. Under review 5harad.com/papers/twiral.pdf, 2014.

[17]

R. J. Hyndman and A. B. Koehler. Another look at measures of forecast accuracy. Int. J. Forecasting, 2006.

[18]

J. Jiang, C. Wilson, X. Wang, P. Huang, W. Sha, Y. Dai, and B. Y. Zhao. Understanding latent interactions in online social networks. In IMC, 2010.

Digital Library

[19]

D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003.

Digital Library

[20]

W. O. Kermack and A. G. McKendrick. Contributions to the mathematical theory of epidemics. II. The problem of endemicity. Proc. of the Royal society of London. Series A, 1932.

[21]

J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Trans. Web, 2007.

Digital Library

[22]

J. Leskovec, D. Huttenlocher, and J. Kleinberg. Signed networks in social media. In CHI, 2010.

Digital Library

[23]

J. Leskovec, J. Kleinberg, and C. Faloutsos. Graph evolution: Densification and shrinking diameters. ACM Trans. KDD, 2007.

Digital Library

[24]

J. Leskovec, K. Lang, A. Dasgupta, and M. Mahoney. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. J. Int. Math., 2009.

[25]

N. Litvak and R. van der Hofstad. Uncovering disassortativity in large scale-free networks. Phys. Review E, 2013.

[26]

D. Maki and M. Thompson. Mathematical models and applications: with emphasis on the social, life, and management sciences. Prentice-Hall, 1973.

[27]

Y. Matias, J. S. Vitter, and W.-C. Ni. Dynamic generation of discrete random variates. In SODA, 1993

Digital Library

[28]

M. E. J. Newman. Mixing patterns in networks. Phys. Rev. E, 2003.

[29]

B. Pittel. On spreading a rumor. SIAM J. Appl. Math., 1987.

Digital Library

[30]

A. Reka and Barabási. Statistical mechanics of complex networks. Rev. Mod. Phys., 2002.

[31]

M. Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. International Semantic Web Conference, 2003.

Digital Library

[32]

L. Takac and M. Zabovsky. Data analysis in public social networks. In Int. Scientific Conf. Present Day Trends of Innovations, 2012.

[33]

J. Ugander, B. Karrer, L. Backstrom, and C. Marlow. The anatomy of the facebook social graph. CoRR, 2011.

[34]

B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in facebook. In WOSN, 2009.

Digital Library

[35]

A. J. Walker. An efficient method for generating discrete random variables with general distributions. ACM Trans. Math. Softw., 1977.

Digital Library

[36]

C. Wong and M. Easton. An efficient method for weighted sampling without replacement. SIAM J. on Computing, 1980.

[37]

N. Wormald. Models of random regular graphs. In Surveys in Combinatorics. Cambridge U. Press, 1999.

Cited By

Zhang FJiang MWang S(2023)Efficient Dynamic Weighted Set Sampling and Its ExtensionProceedings of the VLDB Endowment10.14778/3617838.361784017:1(15-27)Online publication date: 4-Dec-2023
https://doi.org/10.14778/3617838.3617840
Granese FGorla DPalamidessi C(2021)Enhanced models for privacy and utility in continuous-time diffusion networksInternational Journal of Information Security10.1007/s10207-020-00530-7Online publication date: 2-Jan-2021
https://doi.org/10.1007/s10207-020-00530-7
Piskorec MSmuc TSikic M(2019)Disentangling Sources of Influence in Online Social NetworksIEEE Access10.1109/ACCESS.2019.29407627(131692-131704)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2940762
Show More Cited By

Index Terms

Spreading rumours without the network
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

COSN '14: Proceedings of the second ACM conference on Online social networks

October 2014

288 pages

ISBN:9781450331982

DOI:10.1145/2660460

General Chair:
Alessandra Sala
Bell Laboratories, Ireland
,
Program Chairs:
Ashish Goel
Stanford / Twitter, USA
,
Krishna Gummadi
MPI-SWS, Germany

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

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Published: 01 October 2014

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COSN'14: Conference on Online Social Networks

October 1 - 2, 2014

Dublin, Ireland

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COSN '14 Paper Acceptance Rate 25 of 87 submissions, 29%;

Overall Acceptance Rate 69 of 307 submissions, 22%

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Cited By

Zhang FJiang MWang S(2023)Efficient Dynamic Weighted Set Sampling and Its ExtensionProceedings of the VLDB Endowment10.14778/3617838.361784017:1(15-27)Online publication date: 4-Dec-2023
https://doi.org/10.14778/3617838.3617840
Granese FGorla DPalamidessi C(2021)Enhanced models for privacy and utility in continuous-time diffusion networksInternational Journal of Information Security10.1007/s10207-020-00530-7Online publication date: 2-Jan-2021
https://doi.org/10.1007/s10207-020-00530-7
Piskorec MSmuc TSikic M(2019)Disentangling Sources of Influence in Online Social NetworksIEEE Access10.1109/ACCESS.2019.29407627(131692-131704)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2940762
Stück DHallgrímsson HVer Steeg GEpasto AFoschini LBarrett RCummings RAgichtein EGabrilovich E(2017)The Spread of Physical Activity Through Social NetworksProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052688(519-528)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3038912.3052688
Wegrzycki KSankowski PPacuk AWygocki PBarrett RCummings RAgichtein EGabrilovich E(2017)Why Do Cascade Sizes Follow a Power-Law?Proceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052565(569-576)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3038912.3052565
Ryczko KDomurad ABuhagiar NTamblyn I(2017)Hashkat: large-scale simulations of online social networksSocial Network Analysis and Mining10.1007/s13278-017-0424-77:1Online publication date: 4-Feb-2017
https://doi.org/10.1007/s13278-017-0424-7
Garetto MLeonardi ETorrisi G(2016)Generalized Threshold-Based Epidemics in Random GraphsACM SIGMETRICS Performance Evaluation Review10.1145/2964791.290145544:1(37-50)Online publication date: 14-Jun-2016
https://dl.acm.org/doi/10.1145/2964791.2901455
Pacuk ASankowski PWegrzycki KWygocki PBlustein JHerder ERubart JAshman H(2016)There is Something Beyond the Twitter NetworkProceedings of the 27th ACM Conference on Hypertext and Social Media10.1145/2914586.2914623(279-284)Online publication date: 10-Jul-2016
https://dl.acm.org/doi/10.1145/2914586.2914623
Garetto MLeonardi ETorrisi GAlouf SJean-Marie AHegde NProutière A(2016)Generalized Threshold-Based Epidemics in Random GraphsProceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science10.1145/2896377.2901455(37-50)Online publication date: 14-Jun-2016
https://dl.acm.org/doi/10.1145/2896377.2901455

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