The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Friendship and mobility: user movement in location-based social networks
Abstract
Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Limits of Predictability in Human Mobility
Song, C., Qu, Z., Blumm, N., & Barabási, A.-L. (2010) · Science · doi:10.1126/science.1177170 · Cited by 3,234
Cited by
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
GNNExplainer: Generating Explanations for Graph Neural Networks
Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019) · arXiv · doi:10.48550/arxiv.1903.03894 · Cited by 850
Self-citationDecentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625
“[72] 2022 Decentralized recommender system based on the principles of gossip learning Cross-device Decentralized Simulation GMF PRME-G FedAvg FedFast Reptile Synchronous Scheduling Foursqueare-NYC [193] Gowalla-NYC [194] MovieLens [195] [73] 2022 Gossip-based system to maximize social awareness among nodes, improving…”
MethodWhen Machine Learning Meets Privacy: A Survey and Outlook
Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., & Lin, Z. (2022) · ACM Computing Surveys · doi:10.1145/3436755 · Cited by 455
Text-Based Twitter User Geolocation Prediction
Han, B., Cook, P., & Baldwin, T. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4200 · Cited by 324
Mapping Cilento: using geotagged social media data to characterize tourist flows in southern Italy
Chua, A., Servillo, L., Marcheggiani, E., & Moere, A. V. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.06.013 · Cited by 216
A Generative Model of Urban Activities from Cellular Data
Yin, M., Sheehan, M., Feygin, S., Paiement, J.-F., & Pozdnoukhov, A. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2695438 · Cited by 185
A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks
Noulas, A., Scellato, S., Lathia, N., & Mascolo, C. (2012) · 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing · doi:10.1109/socialcom-passat.2012.70 · Cited by 184
Competitive Caching with Machine Learned Advice
Lykouris, T., & Vassilvitskii, S. (2021) · Journal of the ACM · doi:10.1145/3447579 · Cited by 172
“This dataset is publicly available at [CML11, Bri].”
MethodGenerative Adversarial Networks for Spatio-temporal Data: A Survey
Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K. K., Prabowo, A., Rahaman, M. S., & Salim, F. D. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474838 · Cited by 127
Pappalardo, L., Simini, F., Barlacchi, G., & Pellungrini, R. (2022) · Journal of Statistical Software · doi:10.18637/jss.v103.i04 · Cited by 102
Examining Gender Differences of Social Media Use for Activity Planning and Travel Choices
Karatsoli, M., & Nathanail, E. (2020) · European Transport Research Review · doi:10.1186/s12544-020-00436-4 · Cited by 86
How and why people synchronize: An integrated perspective
daSilva, E. B., & Wood, A. (2025) · Personality and Social Psychology Review · doi:10.1177/10888683241252036 · Cited by 56
On the validity of geosocial mobility traces
Zhang, Z., Zhou, L., Zhao, X., Wang, G., Su, Y., Metzger, M., Zheng, H., & Zhao, B. Y. (2013) · Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks · doi:10.1145/2535771.2535786 · Cited by 52
coMobile: real-time human mobility modeling at urban scale using multi-view learning
Zhang, D., Zhao, J., Zhang, F., & He, T. (2015) · Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/2820783.2820821 · Cited by 36
Bodaghi (2017) · doi:10.1177/0266666916641178 · Cited by 15
Higher Education Check-Ins: Exploring the User Experience of Hybrid Location Sensing
Huang, Y., Sang, Y., Wu, Q., & Yao, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359168 · Cited by 12
What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction
Sun, H., Cao, C., Chu, X., Hu, T., Lu, J., He, L., Wang, Z., He, H., & Xiong, H. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3625234 · Cited by 10
An individual-level ground truth dataset for home location detection
Pappalardo, L., Ferres, L., Sacasa, M., Cattuto, C., & Bravo, L. (2020) · arXiv · doi:10.48550/arxiv.2010.08814 · Cited by 7
A Mobility Forecasting Framework with Vertical Federated Learning
Errounda, F. Z., & Liu, Y. (2022) · 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00050 · Cited by 6
Metadata
- Authors
- Cho, E., Myers, S. A., & Leskovec, J.
- Venue
- Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2011-08-21
- Pages
- 1082-1090
- OpenAlex
- W2110953678
- Cited by
- 2,903
- References
- 1
- FWCI
- 262.09
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.