  The emergence of location-based social networks provides an unprecedented
chance to study the interaction between human mobility and social relations.
This work is a step towards quantifying whether a location is suitable for
conducting social activities, and the notion is named location sociality. Being
able to quantify location sociality creates practical opportunities such as
urban planning and location recommendation. To quantify a location's sociality,
we propose a mixture model of HITS and PageRank on a heterogeneous network
linking users and locations. By exploiting millions of check-in data generated
by Instagram users in New York and Los Angeles, we investigate the relation
between location sociality and several location properties, including location
categories, rating and popularity. We further perform two case studies, i.e.,
friendship prediction and location recommendation, experimental results
demonstrate the usefulness of our quantification.
