دانلود رایگان مقاله قدرت روابط غیرمستقیم

abstract

While direct social ties have been intensely studied in the context of computer-mediated social networks, indirect ties (e.g., friends of friends) have seen little attention. Yet in real life, we often rely on friends of our friends for recommendations (of good doctors, good schools, or good babysitters), for introduction to a new job opportunity, and for many other occasional needs. In this work we attempt to 1) quantify the strength of indirect social ties, 2) validate the quantification, and 3) empirically demonstrate its usefulness for applications on two examples. We quantify social strength of indirect ties using a measure of the strength of the direct ties that connect two people and the intuition provided by the sociology literature. We evaluate the proposed metric by framing it as a link prediction problem and experimentally demonstrate that our metric accurately (up to 87.2%) predicts link’s formation. We show via data-driven experiments that the proposed metric for social strength can be used successfully for social applications. Specifically, we show that it can be used for predicting the effects of information diffusion with an accuracy of up to 0.753. We also show that it alleviates known problems in friend-to-friend storage systems by addressing two previously documented shortcomings: reduced set of storage candidates and data availability correlations.

8. Summary and discussions

In this paper, we introduced a social strength metric to measure the strength of indirect social ties by considering both the intensity of interactions and the number of connected paths. We showed that our metric is effective in predicting links formation (can achieve 0.881 prediction accuracy), indicating that it is an accurate quantification of the intensity of an indirect social relationship.Further, we proved our proposed metric’s applicability to two socially informed applications: predicting information diffusion in a social graph and friend-to-friend storage sharing systems. Based on empirical data, our experimental evaluations demonstrate that using the social strength metric is beneficial in both cases. First, social strength accurately predicts information diffusion paths at least 2 steps ahead, which enables intervention mechanisms for rumor squelching and targeted information injection. Second, for the average user in the social graph, it helps identify indirectly connected peers with whom the user has a significant social strength that could act as social incentive in a resource sharing environment, thus significantly increasing the pool of resources available to the user. Third, because indirect ties diversify the pool of users (in this case, by covering more time zones), resource availability increases significantly. A variety of socially aware applications can benefit from the social strength metric. For example, link prediction based on social strength could discover more potentially useful contacts and improve link recommendation accuracy. Automatically setting default privacy controls based on social strength is likely to be more accurate than using graph distance alone. Employing social strength in graph partitioning will have the benefits of relying on local computation, thus allowing for more decentralized and scalable algorithms. Finally, in decentralized OSNs, users’ augmented social strength-based friendsets could provide a more efficient and privacy-guaranteed technique to propagate updates in the presence of churn.