Abstract
With the emerging of location-based social networks, study on the relationship between human mobility and social relationships becomes quantitatively achievable. Understanding it correctly could result in appealing applications, such as targeted advertising and friends recommendation. In this paper, we focus on mining users’ relationship based on their mobility information. More specifically, we propose to use distance between two users to predict whether they are friends. We first demonstrate that distance is a useful metric to separate friends and strangers. By considering location popularity together with distance, the difference between friends and strangers gets even larger. Next, we show that distance can be used to perform an effective link prediction. In addition, we discover that certain periods of the day are more social than others. In the end, we use a machine learning classifier to further improve the prediction performance. Extensive experiments on a Twitter dataset collected by ourselves show that our model outperforms the state-of-the-art solution by 30%.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
González, M., Hidalgo, C., Barabási, A.L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)
Song, C., Koren, T., Wang, P., Barabási, A.L.: Modelling the scaling properties of human mobility. Nature Physics 6(10), 818–823 (2010)
Simini, F., González, M., Maritan, A., Barabási, A.L.: A universal model for mobility and migration patterns. Nature 484, 96–100 (2012)
Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semantic trajectories: Mobility data computation and annotation. ACM Transactions on Intelligent Systems and Technology 4(3), 49 (2013)
Ying, J.J.C., Lee, W.C., Tseng, V.S.: Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM Transactions on Intelligent Systems and Technology 5(1), 2 (2013)
Chen, X., Pang, J., Xue, R.: Constructing and comparing user mobility profiles for location-based services. In: Proc. 28th ACM Symposium on Applied Computing (SAC), pp. 261–266. ACM (2013)
Chen, X., Pang, J., Xue, R.: Constructing and comparing user mobility profiles. ACM Transactions on the Web 8(4), 21 (2014)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proc. 17th ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 1082–1090. ACM (2011)
Backstrom, L., Sun, E., Marlow, C.: Find me if you can: Improving geographical prediction with social and spatial proximity. In: Proc. 19th International Conference on World Wide Web (WWW), pp. 61–70. ACM (2010)
McGee, J., Caverlee, J., Cheng, Z.: Location prediction in social media based on tie strength. In: Proc. 22nd ACM International Conference on Information & Knowledge Management (CIKM), pp. 459–468. ACM (2013)
Cranshaw, J., Toch, E., Hone, J., Kittur, A., Sadeh, N.: Bridging the gap between physical location and online social networks. In: Proc. 12th ACM International Conference on Ubiquitous Computing (UbiComp), pp. 119–128. ACM (2010)
Wang, H., Li, Z., Lee, W.C.: PGT: Measuring mobility relationship using personal, global and temporal factors. In: Proc. 14th IEEE International Conference on Data Mining (ICDM), pp. 570–579. IEEE (2014)
Crandalla, D.J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences 107(52), 22436–22441 (2010)
Pham, H., Shahabi, C., Liu, Y.: EBM: an entropy-based model to infer social strength from spatiotemporal data. In: Proc. 2013 ACM International Conference on Management of Data (SIGMOD), pp. 265–276. ACM (2013)
Tang, J., Chang, Y., Liu, H.: Mining social media with social theories: a survey. ACM SIGKDD Explorations Newsletter 15(2), 20–29 (2014)
Pang, J., Zhang, Y.: Exploring communities for effective location prediction. In: Proc. 24th World Wide Web Conference (Companion Volume) (WWW), pp. 87–88. ACM (2015)
Gao, H., Tang, J., Hu, X., Liu, H.: Content-aware point of interest recommendation on location-based social networks. In: Proc. 29th AAAI Conference on Artificial Intelligence (AAAI). The AAAI Press (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, Y., Pang, J. (2015). Distance and Friendship: A Distance-Based Model for Link Prediction in Social Networks. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-25255-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25254-4
Online ISBN: 978-3-319-25255-1
eBook Packages: Computer ScienceComputer Science (R0)