Abstract
Inferring social relationship based on co-occurrence has become a focal point in the last decade. Many studies indicate that more frequently two users co-occur at non-public locations, the higher probability they are acquaintances. We find that in some spatiotemporal datasets collected by Internet of Things (IOT) devices in public locations, it’s hard to distinguish co-occurrences between acquaintances and strangers. In this paper, we propose a mobility intention-based relationship inference model (MIRI) to address above challenge. We utilize mobility intention to characterize co-occurrences and propose a classification model for social relationship inference. The experimental results on real-world dataset demonstrate not only the superiority of our model, but also improve the effectiveness.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61572231), the Major R&D Plan of Beijing Municipal Science & Technology Commission (Grant No. Z161100002616032), the Security Detection and Supervision for High Level Bio-Safety Laboratory Control System (Grant No. CXJJ-16Z234), the National Defense Basic Research Program of China (Grant No. JCKY2016602B001).
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Yi, F., Li, H., Wang, H., Wen, H., Sun, L. (2017). Mobility Intention-Based Relationship Inference from Spatiotemporal Data. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_75
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DOI: https://doi.org/10.1007/978-3-319-60033-8_75
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