Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems

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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems ( online-social-networks-measurement-analysis-and-applications )

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176 tained due to privacy restrictions. For instance, in the LinkedIn [95] professional networking site, users “vouch” for each other; link formation requires the consent of both parties and users tend to refuse to accept invitations from people they do not know and trust. But, unlike YouTube, it is not possible to crawl the LinkedIn network. However, we were able to obtain the degree distribution of users in the LinkedIn network. We found that both YouTube and LinkedIn degree distributions follow the power-law with similar coefficients. We used maximum-likelihood testing to calculate the coefficients of the YouTube and LinkedIn graphs, and found them to be 1.66 and 1.58 (the resultant Kolmogorov-Smirnov goodness-of-fit metrics were 0.12 and 0.05, suggesting a good fit). This result, along with the previously observed similarity in online social networks’ structure [105], leads us to expect that the overall structure of the YouTube network is similar to trust-based social networks like LinkedIn. Despite their structural similarity, the YouTube social network differs from the LinkedIn trust network in one important aspect: some users in YouTube collect many links (one user had a degree of over 20,000!). The maximum degree of users in actual trust-based social networks tends to be much smaller. Anthropological studies [47] have shown that the average number of relationships a human can actively maintain in the real world is about 150 to 200. Because the amount of unwanted communication a user can send in Ostra is proportional to her degree in the trust network, the results of our YouTube-based evaluation may understate the performance of Ostra on a real

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