The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Human mobility, social ties, and link prediction
Abstract
Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals' movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.
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Cites
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Self-citationFriends and neighbors on the Web
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Cited by
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. (2012) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2012-01703-3 · Cited by 2,424
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Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
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Urban Sensing Using Mobile Phone Network Data: A Survey of Research
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LCARS: a location-content-aware recommender system
Yin, H., Sun, Y., Cui, B., Hu, Z., & Chen, L. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487608 · Cited by 331
Metadata
- Authors
- Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabasi, A.-L.
- Venue
- Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2011-08-21
- Pages
- 1100-1108
- OpenAlex
- W2101108259
- Cited by
- 665
- References
- 30
- FWCI
- 44.47
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabasi, A.-L. (2011). Human mobility, social ties, and link prediction. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2020408.2020581
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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