University of Twente Student Theses
A methodology for deriving aggregate social tie strengths from mobility traces
Brugman, Tristan (2017) A methodology for deriving aggregate social tie strengths from mobility traces.
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| Abstract: | One of the defining characteristics of a location is how well its visitors know each other; knowledge of this "social connectedness" could enable a variety of important applications, such as improved elderly care and socially aware smart phone applications. Previous studies have devised methods to infer the social tie strengths of visitors from information specific to certain communication protocols, but they cannot be used by devices that do not use these protocols. In this thesis, we propose a novel, generally applicable method to infer aggregate social tie strengths from device mobility data. The method works by training a regressor on a subset of a large collection of mobility features and known social tie strengths. Then, this regressor predicts the social tie strengths for devices pairs, and outputs an aggregate score. This method is evaluated based on a real-world data set gathered by Wi-Fi sensors. We found that the accuracy of the proposed method highly outperforms that of a state-of-the-art baseline methodology based on a recent study. Additionally, we tested the method on several modifications of the real-world data set, in order to simulate more difficult environments. On these data sets, too, the method maintains a high accuracy, signifying its robustness. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science MSc (60300) |
| Link to this item: | https://purl.utwente.nl/essays/71973 |
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