On the network effect in Web 2.0 applications q Charu C. Aggarwal a,⇑ , Philip S. Yu b a IBM T. J. Watson Research Center, Hawthorne, NY, United States b University of Illinois at Chicago, Chicago, IL, United States article info Article history: Available online 10 November 2011 Keywords: Auctions Network effect abstract In recent years, the proliferation of the world wide web has lead to an increase in a number of applica- tions such as search, social networks and auctions, whose success depends critically upon the number of users of that service. Two examples of such applications are internet auctions and social networks. One of the characteristics of online auctions is that a successful implementation requires a high volume of buy- ers and sellers at its website. Consequently, auction sites which have a high volume of traffic have an advantage over those in which the volume is limited. This results in even greater polarization of buyers and sellers towards a particular site. The same is true for social networks in which greater use of a given social network increases the use from other participants on the network. This is often referred to as the ‘‘network effect’’ in a variety of interaction-centric applications in networks. While this effect has qual- itatively been known to increase the value of the overall network, its effect has never been modeled or studied rigorously. In this paper, we construct a Markov model to analyze the network effect in the case of two important classes of web applications. These correspond to auctions and social networks. We show that the network effect is very powerful and can result in a situation in which an auction or a social net- working site can quickly overwhelm its competing sites. Thus, the results of this paper show the tremen- dous power of the network effect for Web 2.0 applications. Ó 2011 Elsevier B.V. All rights reserved. 1. Introduction With the increasing use of the world wide web, a number of applications have been developed which are critically dependent upon their adoption by a large number of users. Examples of such applications include online auctions, social networking sites, and product recommendation sites. A key characteristic of these sites is that the presence of a larger number of users increases the value of the site for all other users. A number of popular sites such as Ebay, 1 and Priceline.com 2 routinely conduct auctions on the web in order to match buyers and sellers over a variety of products. Simi- larly, a number of social networking sites such as Facebook, 3 Twitter 4 and LinkedIn 5 have developed over time. Consequently, a number of papers have been written in recent years which study the dynamics of applications such as auctions and social networking (Alsemgeest et al., 1998; Bapna et al., 2000, 2001; Huhns and Vidal, 1999; Klein and O’Keefe, 1999; Klemperar, 1999; Lucking-Reiley, 2000; Shah et al., 2003; Van Hecjk and Vervest, 1998; Ward and Clark, 2002; Wolfstetter, 1996). In many web applications, the success of the site highly de- pends upon the number of users at the site. For example, a social networking site is not very useful if it has a small number of users. Similarly, an auction site is useful only if it has a large number of buyers and sellers who are ready to perform transactions with one another. Therefore the value of these sites greatly increase with the number of users. This is known as the network effect (Shapiro and Vairian, 1998). The network effect of the auction sys- tem continues to grow in a self-sustaining way with site popular- ity. Such a network effect is also present in any system such as community-based search engines 6 in which the quality of the results can depend upon the number of users utilizing the system. For the case of social networks, dense networks have a clear advantage, since it leads to rapid dissemination of information across the net- work (Chakrabarti et al., 2008; Wasserman and Faust, 1994). Large social networks have seen a rapid increase in densification and num- ber of participants in recent years (Leskovec et al., 2005). We note that the exact value, sustainability and impact of the network effect deeply depends upon how it is leveraged for a particular application. In general, the network effect is likely to be experienced in any appli- cation where the value of the network depends upon the level of interaction between the different users. 1567-4223/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.elerap.2011.11.001 q This paper is an extended version of Aggarwal and Yu (2009). ⇑ Corresponding author. E-mail addresses: charu@us.ibm.com (C.C. Aggarwal), psyu@cs.uic.edu (P.S. Yu). 1 http://www.ebay.com. 2 http://www.priceline.com. 3 http://www.facebook.com. 4 http://www.twitter.com. 5 http://www.linkedin.com. 6 http://www.alexa.com. Electronic Commerce Research and Applications 11 (2012) 142–151 Contents lists available at SciVerse ScienceDirect Electronic Commerce Research and Applications journal homepage: www.elsevier.com/locate/ecra