Kun Tu
ABSTRACT
Detecting and tracking groups in mobility network traces is critical for developing accurate mobility models, which in turn are needed for mobile/wireless network design. One approach is to represent mobility traces as a temporal network and apply group (community) detection algorithms to it. However, observing detailed changes in a group over time requires analyzing group dynamics at small time scales and introduces two challenges: (a) group connectivity may be too sparse for group detection; and (b) tracking evolving groups and their lifetimes is difficult. We proposes a group detection framework to address these time scale challenges. For the time-dependent aspect of the groups, we propose a time series segmentation algorithm to detect their formations, dissolutions, and lifetimes. We generate synthetic datasets for mobile networks and use real-world datasets to test our method against state-of-the-art. The results show that our proposed approach achieves more accurate fine-grained group detection than competing methods.
- Amr Ahmed and Eric P Xing. 2008. Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process: with Applications to Evolutionary Clustering.. In SDM. SIAM, 219--230.Google Scholar
- Edoardo M Airoldi, David M Blei, Stephen E Fienberg, and Eric P Xing. 2008. Mixed membership stochastic blockmodels. Journal of Machine Learning Research 9, Sep (2008), 1981--2014. Google ScholarDigital Library
- Christos Anagnostopoulos, Stathes Hadjiefthymiades, and Kostas Kolomvatsos. 2015. Time-optimized user grouping in Location Based Services. Computer Networks 81 (2015), 220--244. Google ScholarDigital Library
- Danielle S Bassett, Mason A Porter, Nicholas F Wymbs, Scott T Grafton, Jean M Carlson, and Peter J Mucha. 2013. Robust detection of dynamic community structure in networks. Chaos: An Interdisciplinary Journal of Nonlinear Science 23, 1 (2013), 013142.Google ScholarCross Ref
- Daniel Boston, Steve Mardenfeld, Juan Susan Pan, Quentin Jones, Adriana Iamnitchi, and Cristian Borcea. 2014. Leveraging Bluetooth co-location traces in group discovery algorithms. Pervasive and Mobile Computing 11 (2014), 88--105.Google ScholarCross Ref
- Deepayan Chakrabarti, Ravi Kumar, and Andrew Tomkins. 2006. Evolutionary clustering. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 554--560. Google ScholarDigital Library
- Yung-Chih Chen, Elisha Rosensweig, Jim Kurose, and Don Towsley. 2010. Group detection in mobility traces. In Proceedings of the 6th international wireless communications and mobile computing conference. ACM, 875--879. Google ScholarDigital Library
- Wenjie Fu, Le Song, and Eric P. Xing. 2009. Dynamic Mixed Membership Block-model for Evolving Networks. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09). ACM, New York, NY, USA, 329--336. Google ScholarDigital Library
- Laetitia Gauvin, AndrÃl' Panisson, and Ciro Cattuto. 2014. Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach. PloS one 9, 1 (2014), e86028.Google ScholarCross Ref
- So Hirai and Kenji Yamanishi. 2012. Detecting changes of clustering structures using normalized maximum likelihood coding. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 343--351. Google ScholarDigital Library
- Inah Jeon, Evangelos E Papalexakis, Christos Faloutsos, Lee Sael, and U Kang. 2016. Mining billion-scale tensors: algorithms and discoveries. The VLDB Journal 25, 4 (2016), 519--544. Google ScholarDigital Library
- David J Ketchen and Christopher L Shook. 1996. The application of cluster analysis in strategic management research: an analysis and critique. Strategic management journal 17, 6 (1996), 441--458.Google Scholar
- Min-Soo Kim and Jiawei Han. 2009. A particle-and-density based evolutionary clustering method for dynamic networks. Proceedings of the VLDB Endowment 2, 1 (2009), 622--633. Google ScholarDigital Library
- Stefano Leonardi, Aris Anagnostopoulos, Jakub Lacki, Silvio Lattanzi, and Mohammad Mahdian. 2016. Community Detection on Evolving Graphs. In Advances in Neural Information Processing Systems. 3522--3530.Google Scholar
- Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L Tseng. 2008. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In Proceedings of the 17th international conference on World Wide Web. ACM, 685--694. Google ScholarDigital Library
- Philippe Nain, Don Towsley, Benyuan Liu, and Zhen Liu. 2005. Properties of random direction models. In INFOCOM 2005, 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Proceedings IEEE, Vol. 3. IEEE, 1897--1907.Google ScholarCross Ref
- Evangelos E Papalexakis, Konstantinos Pelechrinis, and Christos Faloutsos. 2015. Location based social network analysis using tensors and signal processing tools. In Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 IEEE 6th International Workshop on. IEEE, 93--96.Google ScholarCross Ref
- Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53--65. Google ScholarDigital Library
- Vedran Sekara, Arkadiusz Stopczynski, and Sune Lehmann. 2016. Fundamental structures of dynamic social networks. Proceedings of the National Academy of Sciences 113, 36 (2016), 9977--9982.Google ScholarCross Ref
- Lei Tang, Huan Liu, Jianping Zhang, and Zohreh Nazeri. 2008. Community evolution in dynamic multi-mode networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 677--685. Google ScholarDigital Library
- Kun Tu, Bruno Ribeiro, Ananthram Swami, and Don Towsley. 2016. Temporal Clustering in Dynamic Networks with Tensor Decomposition. arXiv preprint arXiv:1605.08074 (2016).Google Scholar
- Lijun Wang, Manjeet Rege, Ming Dong, and Yongsheng Ding. 2012. Low-rank kernel matrix factorization for large-scale evolutionary clustering. Knowledge and Data Engineering, IEEE Transactions on 24, 6 (2012), 1036--1050. Google ScholarDigital Library
- Kevin S Xu, Mark Kliger, and Alfred O Hero. 2010. Evolutionary spectral clustering with adaptive forgetting factor. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on. IEEE, 2174--2177.Google ScholarCross Ref
- Yangyang Xu and Wotao Yin. 2013. A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM Journal on imaging sciences 6, 3 (2013), 1758--1789.Google Scholar
- Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong, and Rong Jin. 2011. Detecting communities and their evolutions in dynamic social networks: a Bayesian approach. Machine learning 82, 2 (2011), 157--189. Google ScholarDigital Library
- Wenchao Yu, Charu C Aggarwal, and Wei Wang. 2017. Temporally Factorized Network Modeling for Evolutionary Network Analysis. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 455--464. Google ScholarDigital Library
Index Terms
- Tracking Groups in Mobile Network Traces
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