Citation: | Junfeng TIAN and Zhengqi HOU, “Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–13, 2024 doi: 10.23919/cje.2022.00.363 |
[1] |
E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: User movement in location-based social networks,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp.1082–1090, 2011
|
[2] |
G. Kossinets and D. Watts, “Origins of homophily in an evolving social network,” American Journal of Sociology, vol. 115, no. 2, pp. 405–450, 2009. doi: 10.1086/599247
|
[3] |
D. Z. Ding, M. Zhang, S. Y. Li, et al., “BayDNN: Friend recommendation with Bayesian personalized ranking deep neural network,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Singapore, pp.1479–1488, 2017.
|
[4] |
S. C. Peng, A. M. Yang, L. H. Cao, et al., “Social influence modeling using information theory in mobile social networks,” Information Sciences, vol. 379, pp. 146–159, 2017. doi: 10.1016/j.ins.2016.08.023
|
[5] |
Z. H. Hu, C. Peng, C. He, et al., “IO-aware factorization machine for user response prediction,” in Proceedings of 2020 International Joint Conference on Neural Networks, Glasgow, UK, pp.1–8, 2020.
|
[6] |
J. Leskovec and A. Krevl, “SNAP datasets: Stanford large network dataset collection,” Available at: http://snap.stanford.edu/data, 2014.
|
[7] |
C. He, C. Peng, N. Li, et al., “CIFEF: Combining implicit and explicit features for friendship inference in location-based social networks, ” in Proceedings of the 13th International Conference on Knowledge Science, Engineering and Management, Hangzhou, China, pp. 168–180, 2020.
|
[8] |
Y. Z. Cheng, “Mean shift, mode seeking, and clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790–799, 1995. doi: 10.1109/34.400568
|
[9] |
H. Pham, C. Shahabi, and Y. Liu, “EBM: An entropy-based model to infer social strength from spatiotemporal data,” in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, pp.265–276, 2013.
|
[10] |
H. J. Wang, Z. H. Li, and W. C. Lee, “PGT: Measuring mobility relationship using personal, global and temporal factors,” in Proceedings of 2014 IEEE International Conference on Data Mining, Shenzhen, China, pp.570–579, 2014.
|
[11] |
G. S. Njoo, M. C. Kao, K. W. Hsu, et al., “Exploring check-in data to infer social ties in location based social networks,” in Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, South Korea, pp.460–471, 2017.
|
[12] |
G. S. Njoo, K. W. Hsu, and W. C. Peng, “Distinguishing friends from strangers in location-based social networks using co-location,” Pervasive and Mobile Computing, vol. 50, pp. 114–123, 2018. doi: 10.1016/j.pmcj.2018.09.001
|
[13] |
C. He, C. Peng, N. Li, et al., “Exploiting spatiotemporal features to infer friendship in location-based social networks,” in Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, pp.395–403, 2018.
|
[14] |
A. E. Bayrak and F. Polat, “Mining individual features to enhance link prediction efficiency in location based social networks,” in Proceedings of 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain, pp.920–925, 2018.
|
[15] |
P. H. Wang, F. Y. Sun, D. Wang, et al., “Predicting attributes and friends of mobile users from AP-Trajectories,” Information Sciences, vol. 463-464, pp. 110–128, 2018. doi: 10.1016/j.ins.2018.06.029
|
[16] |
S. Scellato, A. Noulas, and C. Mascolo, “Exploiting place features in link prediction on location-based social networks,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp.1046–1054, 2011.
|
[17] |
M. Backes, M. Humbert, J. Pang, et al., “Walk2friends: Inferring social links from mobility profiles,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA, pp.1943–1957, 2017.
|
[18] |
Q. Gao, G. Trajcevski, F. Zhou, et al., “Trajectory-based social circle inference,” in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA, pp.369–378, 2018.
|
[19] |
F. Zhou, B. Y. Wu, Y. Yang, et al., “Vec2Link: Unifying heterogeneous data for social link prediction,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, pp.1843–1846, 2018.
|
[20] |
L. F. Ren, R. M. Hu, D. S. Li, et al., “Cross-regional friendship inference via category-aware multi-bipartite graph embedding,” in Proceedings of 2022 IEEE 47th Conference on Local Computer Networks, Edmonton, Canada, pp.73–80, 2022.
|
[21] |
Q. Gao, F. Zhou, X. Yang, et al., “When friendship meets sequential human check-ins: Inferring social circles with variational mobility,” Neurocomputing, vol. 518, pp. 174–189, 2023. doi: 10.1016/j.neucom.2022.10.049
|
[22] |
J. Li, F. Z. Zeng, Z. Xiao, et al., “Drive2friends: Inferring social relationships from individual vehicle mobility data,” IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5116–5127, 2020. doi: 10.1109/JIOT.2020.2974669
|
[23] |
J. Li, F. Z. Zeng, Z. Xiao, et al., “Social relationship inference over private vehicle mobility data,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 5221–5233, 2021. doi: 10.1109/TVT.2021.3060787
|
[24] |
F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine learning in python,” The Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
|
[25] |
J. D. Zhang and C. Y. Chow, “Enabling probabilistic differential privacy protection for location recommendations,” IEEE Transactions on Services Computing, vol. 14, no. 2, pp. 426–440, 2021. doi: 10.1109/TSC.2018.2810890
|
[26] |
J. Wang, F. Wang, and H. T. Li, “Differential privacy location protection scheme based on Hilbert curve,” Security and Communication Networks, vol. 2021, article no. 5574415, 2021. doi: 10.1155/2021/5574415
|
[27] |
H. T. Li, L. X. Gong, B. Wang, et al., “k-anonymity based location data query privacy protection method in mobile social networks,” in Proceedings of 2020 International Conference on Networking and Network Applications, Haikou City, China, pp.326–334, 2020.
|