Planetary-scale views on a large instant-messaging network

ABSTRACT

We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender.

References

1. R. Albert, H. Jeong, and A.-L. Barabasi. Error and attack tolerance of complex networks. Nature, 406:378, 2000.
2. J. I. Alvarez-Hamelin, L. Dall'Asta, A. Barrat, and A. Vespignani. Analysis and visualization of large scale networks using the k-core decomposition. In ECCS '05: European Conference on Complex Systems, 2005.
3. D. Avrahami and S. E. Hudson. Communication characteristics of instant messaging: effects and predictions of interpersonal relationships. In CSCW '06, pages 505--514, 2006.
4. A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207, 2005.
5. V. Batagelj and M. Zaversnik. Generalized cores. ArXiv, (cs.DS/0202039), Feb 2002.
6. IDC Market Analysis. Worldwide Enterprise Instant Messaging Applications 2005--2009 Forecast and 2004 Vendor Shares: Clearing the Decks for Substantial Growth. 2005.
7. J. Leskovec and E. Horvitz. Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network. Tech. report MSR-TR-2006-186, 2006.
8. P. V. Marsden. Core discussion networks of americans. American Sociological Review, 52(1):122--131, 1987.
9. M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1):415--444, 2001.
10. B. A. Nardi, S. Whittaker, and E. Bradner. Interaction and outeraction: instant messaging in action. In CSCW '00: Proceedings of the 2000 ACM conference on Computer supported cooperative work, pages 79--88, 2000.
11. E. Ravasz and A.-L. Barabasi. Hierarchical organization in complex networks. Physical Review E, 67(2):026112, 2003.
12. E. M. Rogers and D. K. Bhowmik. Homophily-heterophily: Relational concepts for communication research. Public Opinion Quarterly, 34:523--538, 1970.
13. X. Shi, L. A. Adamic, and M. J. Strauss. Networks of strong ties. Physica A Statistical Mechanics and its Applications, 378:33--47, May 2007.
14. P. Singla and M. Richardson. Yes, there is a correlation - from social networks to personal behavior on the web. In WWW '08, 2008.
15. M. P. Stumpf, C. Wiuf, R. M. May. Subnets of scale-free networks are not scale-free: sampling properties of networks. PNAS, 102(12), 2005.
16. S. L. Tauro, C. Palmer, G. Siganos, and M. Faloutsos. A simple conceptual model for the internet topology. In GLOBECOM '01, vol. 3, pages 1667--1671, 2001.
17. J. Travers and S. Milgram. An experimental study of the small world problem. Sociometry, 32(4), 1969.
18. A. Voida, W. C. Newstetter, and E. D. Mynatt. When conventions collide: the tensions of instant messaging attributed. In CHI '02, pages 187--194, 2002.
19. D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393:440--442, 1998.
20. X. Xiao, L. Guo, and J. Tracey. Understanding instant messaging traffic characteristics. In ICDCS '07, 2007.

Index Terms

• Information systems

  • Information systems applications

    • Data mining