A Co-occurrence Prediction Framework in Location-Based Social Networks

Mehrnoosh Tarafdar, Behrouz Minaei-Bidgoli

Abstract: Nowadays, social networks are inseparable part of people everyday life. Large amounts of check-ins collected by location-based social networks (LBSNs) are rich information sources of social and spatio-temporal aspects of human life. In LBSNs, a co-occurrence refers to an event that a pair (almost) simultaneously appears at the same location. There are very few efforts on co-occurrence prediction in LBSNs in the literature. This paper aims to predict the co-occurrences between pairs of users, whether in the future or the past. This can be used in epidemiological studies like today COVID-19 pandemic. We have identified several effective spatial/temporal/social factors. For each, the method of the predictability measurement has been proposed. The spatial, regional, categorical and hierarchical-temporal aspects have been measured with inverse diversity, which is based on Entropy. In general, lower entropy implies higher predictability. Coincidences are unintentional encounters between users. Unlike previous studies that eliminated them, we have been considered them as a co-occurrence type and their effects have been measured and considered in the prediction task. By utilizing these identified factors along with the friendship information, a novel framework has been developed to enable the co-occurrence pattern inference among user pairs. Furthermore, valid patterns have been identified to prevent predictions based on expired patterns. To the best of our knowledge, this issue has not been studied in any other human behavior investigations. Evaluations with two real LBSN datasets, show the efficacy of our co-occurrence inference framework, outperforming other comparable method.

External IDs:dblp:journals/ngc/TarafdarM24