The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Multidimensional binary search trees used for associative searching
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Indexing Spatio-Temporal Data Warehouses
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Mining user similarity based on location history
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Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards integrating real-world spatiotemporal data with social networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The ubiquity of mobile devices and the popularity of location-based-services have generated, for the first time, rich datasets of people's location information at a very high fidelity. These location datasets can be used to study people's behavior - for example, social studies have shown that people, who are seen together frequently at the same place and at the same time, are most probably socially related. In this paper, we are interested in inferring these social connections by analyzing people's location information, which is useful in a variety of application domains from sales and marketing to intelligence analysis. In particular, we propose an entropy-based model (EBM) that not only infers social connections but also estimates the strength of social connections by analyzing people's co-occurrences in space and time. We examine two independent ways: diversity and weighted frequency, through which co-occurrences contribute to social strength. In addition, we take the characteristics of each location into consideration in order to compensate for cases where only limited location information is available. We conducted extensive sets of experiments with real-world datasets including both people's location data and their social connections, where we used the latter as the ground-truth to verify the results of applying our approach to the former. We show that our approach outperforms the competitors.