Social tie classification using location based social network data, implemented using Python.
If you are interested to apply this work in your research or project, please cite the following work.
Gunarto Sindoro Njoo, Min-Chia Kao, Kuo-Wei Hsu, Wen-Chih Peng: Exploring Check-in Data to Infer Social Ties in Location Based Social Networks. PAKDD (1) 2017: 460-471
Dataset used in the experiments are obtained from LBSN data: Gowalla and Brighkite (http://snap.stanford.edu/data/).
The processed dataset used throughout the SoBA experiments can be found in this link (http://bit.ly/pakdd2017-dataset-social).
The functions needed in other python files are provided here. It is a library for this project.
Provided the definition required for the classes used throughout this project
Co-location generator, the first phase in our work, generating the co-location between check-ins of two users. The co-location implemented in this work apply two parameters: location distance and time difference. As for the location distance, we applied 0m (same venue id) to serve as same location, whereas this parameter can be modified by using the haversine distance between check-ins' venue. Time difference can varies from minutes, hours, days, or even weeks.
Similar with "colocation.py" but accept general location distance (instead of the same venue id). But this version is still in beta and might have some bugs.
Feature extractions of our method as well as the evaluation of our method are implemented here.
Competitor of this work, using PGT framework. H. Wang, Z. Li and W. C. Lee, "PGT: Measuring Mobility Relationship Using Personal, Global and Temporal Factors," 2014 IEEE International Conference on Data Mining, Shenzhen, 2014, pp. 570-579.
The functions needed in other python files are provided here. It is a library for this project.
The evaluation file for the proposed framework.
The evaluation file for PGT method.
Python codes to generate plots.