Abstract
Human behaviors exhibit ubiquitous correlations in many aspects, such as individual and collective levels, temporal and spatial dimensions, content, social and geographical layers. With rich Internet data of online behaviors becoming available, it attracts academic interests to explore human mobility similarity from the perspective of social network proximity. Existent analysis shows a strong correlation between online social proximity and offline mobility similarity, namely, mobile records between friends are significantly more similar than between strangers, and those between friends with common neighbors are even more similar. We argue the importance of the number and diversity of common friends, with a counter intuitive finding that the number of common friends has no positive impact on mobility similarity while the diversity plays a key role, disagreeing with previous studies. Our analysis provides a novel view for better understanding the coupling between human online and offline behaviors, and will help model and predict human behaviors based on social proximity.
Highlights
Human behaviors exhibit ubiquitous correlations in many aspects, such as individual and collective levels, temporal and spatial dimensions, content, social and geographical layers
We find that human mobility similarity is strongly correlated with the existence of social connection and common neighbors
The mobility similarity between a pair of individuals is measured with Spatial Cosine Similarity (SCos), which is the cosine similarity of two individuals’ trajectory vectors
Summary
Human behaviors exhibit ubiquitous correlations in many aspects, such as individual and collective levels, temporal and spatial dimensions, content, social and geographical layers. The explosion of online social network services provides a platform for various kinds of purposes, such as making friends, getting information, or even hunting jobs It gains increasing interests in academia and industry to explore the mutual influence between human mobility and social connections, which has significant value in many fields such as location prediction, location recommendation, friend recommendation[17,18], even social-economic analysis[19,20,21]. In this report we perform a finer analysis to demonstrate what and how social proximity measurements are correlated with mobility similarity between two individuals based on an LBSN dataset (see Methods), in which people share real-time locations (usually referred to as “check-in”) with online friends. Once the existence of online connection and common neighbors is given, the number of common neighbors has no positive impact on mobility similarity, while the higher diversity in common neighbors brings higher similarity in mobility pattern
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