Christo Wilson
Ben Y. Zhao
ABSTRACT
Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone.
This leads to the question: Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially-enhanced applications? In this paper, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of interaction graphs to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the "small-world" properties shown in their social graph counterparts. This means that these graphs have fewer "supernodes" with extremely high degree, and overall network diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate two well-known social-based applications (RE and SybilGuard). The results reveal new insights into both systems, and confirm our hypothesis that studies of social applications should use real indicators of user interactions in lieu of social graphs.
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Index Terms
- User interactions in social networks and their implications
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Reviews
Burkhard Englert
Recently, several applications have been designed that use social networking relationships "to improve [the] security and performance [of] applications such as email, Web browsing, and overlay routing." Such applications usually base trust on the existence of social network connections between participants. In this paper, Wilson et al. demonstrate the limitations of social network connections alone as the basis for trust, and make suggestions for a better approach. They show, based on a large social network and user interaction analysis on the Facebook network, that only a fraction of users' social network connections (friends) represent actual social interactions. Hence, they propose the use of interaction graphs instead of the customary social graphs as the basis of trust decisions in applications. "An interaction graph contains all nodes from its social graph counterpart, but only a subset of the links"-those over which real interactions occur. Social interaction graphs reduce the social graphs and therefore enhance their value. As the authors further demonstrate, this approach leads to improved performance in algorithms such as Reliable Email [1] and SybilGuard [2]. This well-written paper deserves credit for its formal verification of the seemingly intuitive observation that social network connections do not always represent social network interactions, and for its careful analysis of the relationship between social and interaction graphs. One of its main weaknesses is its exclusive focus on Facebook. As a result, the issue of how to construct interaction graphs for other social networking sites is never addressed. In the end, what remains is a valuable suggestion-to consider social interactions instead of social connections-to anyone who wants to develop applications based on social networks. Online Computing Reviews Service
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