Quantifying Location Sociality

TL;DR

This paper introduces a novel method to quantify location sociality using a mixture model of HITS and PageRank on social network data, enabling better urban planning and location-based services.

Contribution

It proposes a new approach to measure location sociality by combining HITS and PageRank on a heterogeneous user-location network, validated with real social media data.

Findings

Location sociality correlates with location categories, ratings, and popularity.

The model effectively predicts friendships and recommends locations.

Quantification of sociality enhances urban planning and social network analysis.

Abstract

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.