Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10 % to 30 % of all human movement, while periodic behavior explains 50 % to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

Online social networking sites like MySpace and Flickr have become a popular way to share and disseminate content. Their massive popularity has led to the viral marketing of content, products, and political campaigns on the sites themselves. Despite the excitement, the precise mechanisms by which information is exchanged over these networks are not well understood. In this paper, we investigate social cascades, or how information disseminates through social links in online social networks. Using real traces of 1,000 popular photos and a social network collected from Flickr, and a theoretical framework borrowed from epidemiology, we show that social cascades are an important factor in the dissemination of content. Our work provides an important first step in understanding how information disseminates in social networks.

Abstract. We address the important question of the extent to which random variables and vectors with truncated power tails retain the characteristic features of random variables and vectors with power tails. We define two truncation regimes, soft truncation regime and hard truncation regime, and show that, in the soft truncation regime, truncated power tails behave, in important respects, as if no truncation took place. On the other hand, in the had truncation regime much of “heavy tailedness” is lost. We show how to estimate consistently the tail exponent when the tails are truncated, and suggest statistical tests to decide on whether the truncation is soft or hard. Finally, we apply our methods to two recent data sets arising from computer networks. 1.