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 article, 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 present in their social graph counterparts. This means that these graphs have fewer “supernodes” with extremely high degree, and overall graph diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate several well-known social-based applications that rely on graph properties to infuse new functionality into Internet applications, including Reliable Email (RE), SybilGuard, and the weighted cascade influence maximization algorithm. The results reveal new insights into each of these systems, and confirm our hypothesis that to obtain realistic and accurate results, ongoing research on social network applications studies of social applications should use real indicators of user interactions in lieu of social graphs.
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Index Terms
- Beyond Social Graphs: User Interactions in Online Social Networks and their Implications
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