Hyunwoo Chun
Haewoon Kwak
ABSTRACT
Online social networking services are among the most popular Internet services according to Alexa.com and have become a key feature in many Internet services. Users interact through various features of online social networking services: making friend relationships, sharing their photos, and writing comments. These friend relationships are expected to become a key to many other features in web services, such as recommendation engines, security measures, online search, and personalization issues. However, we have very limited knowledge on how much interaction actually takes place over friend relationships declared online. A friend relationship only marks the beginning of online interaction.
Does the interaction between users follow the declaration of friend relationship? Does a user interact evenly or lopsidedly with friends? We venture to answer these questions in this work. We construct a network from comments written in guestbooks. A node represents a user and a directed edge a comments from a user to another. We call this network an activity network. Previous work on activity networks include phone-call networks [34, 35] and MSN messenger networks [27]. To our best knowledge, this is the first attempt to compare the explicit friend relationship network and implicit activity network.
We have analyzed structural characteristics of the activity network and compared them with the friends network. Though the activity network is weighted and directed, its structure is similar to the friend relationship network. We report that the in-degree and out-degree distributions are close to each other and the social interaction through the guestbook is highly reciprocated. When we consider only those links in the activity network that are reciprocated, the degree correlation distribution exhibits much more pronounced assortativity than the friends network and places it close to known social networks. The k-core analysis gives yet another corroborating evidence that the friends network deviates from the known social network and has an unusually large number of highly connected cores.
We have delved into the weighted and directed nature of the activity network, and investigated the reciprocity, disparity, and network motifs. We also have observed that peer pressure to stay active online stops building up beyond a certain number of friends.
The activity network has shown topological characteristics similar to the friends network, but thanks to its directed and weighted nature, it has allowed us more in-depth analysis of user interaction.
- L. A. Adamic, J. Zhang, E. Bakshy, and M. S. Ackerman. Knowledge sharing and yahoo answers: Everyone knows something. In WWW '08, pages 665--674. ACM, 2008. Google Scholar
Digital Library
- Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 835--844, New York, NY, USA, 2007. ACM Press. Google ScholarDigital Library
- E. Almaas, B. Kovacs, T. Vicsek, Z. N. Oltvai, and A.-L. Barabasi. Global organization of metabolic fluxes in the bacterium escherichia coli. Nature, 427:839--843, Feb 2003. Letters to Nature.Google ScholarCross Ref
- R. Aukett, J. Ritchie, and K. Mill. Gender differences in friendship patterns. Sex Roles, 19, 1988.Google Scholar
- R. Axelrod and W. Hamilton. The evolution of cooperation. Science, 211(4489):1390--1396, 1981.Google ScholarCross Ref
- A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435:207--211, May 2005.Google ScholarCross Ref
- A. Barrat, M. Barthelemy, R. Pastor-Satorras, and A. Vespignani. The architecture of complex weighted networks. Proceedings of National Academy of Sciences, 101(11):3747--3752, March 16 2004.Google ScholarCross Ref
- C. Bialik. Sorry, you may have gone over your limit of network friends. The Wall street journal, November 16 2007.Google Scholar
- M. E. Crovella and A. Bestavros. Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE /ACM Transactions on Networking, 5(6):835--846, 1997. Google ScholarDigital Library
- J. N. Cummings, B. Butler, and R. Kraut. The quality of online social relationships. Commun. ACM, 45(7):103--108, 2002. Google ScholarDigital Library
- B. Derrida and H. Flyvbjerg. Statistical properties of randomly broken objects and of multivalley structures in disordered systems. Journal of Physics A: Mathematical and General, 20:5273--5288, 1987.Google ScholarCross Ref
- R. Dunbar. Co-evolution of neocortex size, group size, and language in humans. Behavioral and brain scineces, 16(4):681--735, 1993.Google Scholar
- R. Dunbar. Grooming, Gossip, and the Evolution of Language. Harvard University Press, 1998.Google Scholar
- H. Ebel, L.-I. Mielsch, and S. Bornholdt. Scale-free topology of e-mail networks. Phys. Rev. E, 66:035103, 2002.Google ScholarCross Ref
- Y.-H. Eom, C. Jeon, H. Jeong, and B. Kahng. Evolution of weighted scale-free networks in empirical data. accepted in Phys. Rev. E.Google Scholar
- D. Garlaschelli and M. I. Loffredo. Patterns of link reciprocity in directed networks. Physical Review Letters, 93:268701, 2004.Google ScholarCross Ref
- K.-I. Goh, Y.-H. Eom, H. Jeong, B. Kahng, and D. Kim. Structure and evolution of online social relationships: Heterogeneity in unrestricted discussions. Phys. Rev. E, 73:066123, 2006.Google ScholarCross Ref
- S. A. Golder, D. Wilkinson, and B. A. Huberman. Rhythms of social interaction: Messaging within a massive online network. In 3rd International Conference on Communities and Technologies (CT2007). East Lansing, MI., June 2007.Google ScholarCross Ref
- V. Gómez, A. Kaltenbrunner, and V. López. Statistical analysis of the social network and discussion threads in slashdot. In WWW '08: Proceedings of the 17th international conference on World Wide Web, New York, NY, USA, April 2008. ACM. Google ScholarDigital Library
- A. W. Gouldner. The norm of reciprocity: A preliminary statement. American Sociological Review, 25:161--178, april 1960.Google ScholarCross Ref
- M. S. Granovetter. The strength of weak ties. The American Journal of Sociology, 78(6):1360--1380, 1973.Google ScholarCross Ref
- C. K. Hemelrijk. Models of, and tests for, reciprocity, unidirectionality and other social interaction patterns at a group level. Animal Behavior, 39:1013--1029, 1990.Google ScholarCross Ref
- P. Holme, C. R. Edling, and F. Liljeros. Structure and time-evolution of an internet dating community. Social Networks, 26:155, 2004.Google ScholarCross Ref
- A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In WebKDD/SNA-KDD '07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- G. Kossinets and D. Watts. Emprical Analysis of an Evolving Social Network. Science, 311(88), 2006.Google Scholar
- J. Leskovec and C. Faloutsos. Sampling from large graphs. In Proceedings of ACM KDD, 2006. Google ScholarDigital Library
- J. Leskovec and E. Horvitz. Planetary-scale views on an instant-messaging network. http://arxiv.org/abs/0803.0939, Mar 2008. Google ScholarDigital Library
- R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, M. Sheffer, and U. Alon. Superfamilies of Evolved and Designed Networks. Science, 303(5663):1538--1542, 2004.Google Scholar
- A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and Analysis of Online Social Networks. In ACM Internet Measurement Conference, October 2007. Google ScholarDigital Library
- M. E. J. Newman. Assortative mixing in networks. Phys. Rev. Lett., 89(20):208701, Oct 2002.Google ScholarCross Ref
- M. E. J. Newman. Mixing patterns in networks. Phys. Rev. E, 67(2):026126, Feb 2003.Google ScholarCross Ref
- M. A. Nowak. Five Rules for the Evolution of Cooperation. Science, 314(5805):1560--1563, 2006.Google ScholarCross Ref
- J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. A. de Menezes, K. Kaski, A.-L. Barabási, and J. Kertész. Analysis of a large-scale weighted network of one-to-one human communication. New Journal of Physics, 9:179, 2007.Google ScholarCross Ref
- J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási. Structure and tie strengths in mobile communication networks. Proc. Nat. Acad. Sci., 104(18):7332, 2007.Google ScholarCross Ref
- S. S. Shen-Orr, R. Milo, S. Mangan, and U. Alon. Network motifs in the transcriptional regulation network of escherichia coli. Nature Genetics, 31:64--68, 2002.Google ScholarCross Ref
- G. Simmel and K. H. Wolff. The Sociology of Georg Simmel. Free Press, 1950.Google Scholar
- D. Stutzbach, R. Rejaie, N. Duffield, S. Sen, and W. Willinger. On unbiased sampling for unstructured peer-to-peer networks. In IMC '06, pages 27--40, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- R. Thurnwald. Economics in Primitive Communities. Oxford University Press, 1932.Google Scholar
- S. T. Tong, B. V. D. Heide, L. Langwell, and J. B. Walther. Too much of a good thing? the relationship between number of friends and interpersonal impressions on Facebook. Journal of Computer-Mediated Communication, 13:531--549, 2008.Google ScholarCross Ref
- S. Valverde and R. V. Solé. Evolving social weighted networks: Nonlocal dynamics of open source communities. arXiv:physics/0602005v1.Google Scholar
- S. Valverde and R. V. Solé. Self-organization versus hierarchy in open-source social networks. Phys. Rev. E, 76:046118, 2007.Google ScholarCross Ref
- S. Wasserman and K. Faust. Social network analysis. Cambridge University Press, Cambridge, 1994.Google ScholarCross Ref
- S. Wernicke and F. Rasche. Fanmod: a tool for fast network motif detection. Bioinformatics, 22:1152--1153, 2006. Google ScholarDigital Library
- V. Zlatic, M. Bozicevic, H. Stefancic, and M. Domazet. Wikipedias: Collaborative web-based encyclopedias as complex networks, Jul 2006.Google Scholar
- R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594):824--827, 2002.Google Scholar
Index Terms
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