Local community detection for multi-layer mobile network based on the trust relation
- 27 Downloads
With the fast development of mobile Internet, people’s social exchange media has transformed from the traditional social network to mobile network. With the explosion of massive information, it has become an interesting topic to detect network user groups with close correlation in the mobile social network. These groups are hidden in the continuously changing relations of social network, and it is very difficult to obtain the information of entire social network. In addition, these social relations are intertwined and complicated under the influence of various networks, and as a result, researches on single-layer network are simple and incomplete. Therefore, this paper proposed a local community detection algorithm for multi-layer complicated network based on the trust relation (MTLCD) to constrain the node tensor. We compared the performance of our algorithm with other classic network clustering algorithms such as GL, LART and PMM in four actual multi-layer network datasets of Bio GRID, Remote sensing, Twitter and Mobile QQ Zone, and the multi-layer modularity was used as the measurement index to evaluate the algorithm performance. The experimental results and analysis prove that: in the MTLCD algorithm, the core node obtained based on the trust relation can better identify the local community in dataset with trust relation. In addition, we also found that this algorithm had higher accuracy and stability, and it can accurately reflect the local community structure which the core node belongs to.
KeywordsMobile social multi-layer network Local community detection Trust relation Tensor
This work was supported by the Fundamental Research of Xinjiang Corps 2016AC015, and the Applied Basic Research Project of Qinghai Province No: 2018-ZJ-707, and the Youth Foundation of Shanghai Polytechnic University under Grant No. EGD18XQD01; the CERNET Innovation Project No. NGII2017 0513.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 3.Goffman, E. (1974). Frame analysis: An essay on the organization of experience. Cambridge: Harvard University Press.Google Scholar
- 5.Wang, W., Li, X., Jiao, P., et al. (2017). Exploring intracity taxi mobility during the holidays for location-based marketing. Mobile Information Systems, 2017, 1.Google Scholar
- 12.Dunlavy, D. M., Kolda, T. G., & Kegelmeyer, W. P. (2011). Multilinear algebra for analyzing data with multiple linkages. In J. Kepner & J. Gilbert (Eds.), Graph algorithms in the language of linear algebra (pp. 85–114). SIAM.Google Scholar
- 17.Li, X. M., Yuan, L., Liu, C. C., et al. (2017). An Efficient Critical Incident Propagation Model for Social Networks Based on Trust Factor. In International conference on collaborative computing: Networking, applications and worksharing (pp. 416–424). Springer, Cham.Google Scholar
- 19.Al-Sharoa, E., Al-khassaweneh, M., & Aviyente, S. (2017). A tensor based framework for community detection in dynamic networks. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 2312–2316). IEEE.Google Scholar
- 22.Ma, Y., Lu, H., Gan, Z., & Zhao, Y. (2014). Trust inference path search combining community detection and ant colony optimization. In International conference on web-age information management (pp. 687–698). Springer, Cham.Google Scholar
- 24.Beigi, G., Jalili, M., Alvari, H., & Sukthankar G. (2014). Leveraging community detection for accurate trust prediction. In 2014 ASE international conference on social computing.Google Scholar
- 26.Cao, C., Ni, Q., and Zhai, Y. (2015). An effective recommendation model based on communities and trust network. In 2015 IEEE 27th international conference on tools with artificial intelligence (ICTAI) (pp. 1029–1036). IEEE.Google Scholar
- 33.Gao, H., Zhang, K., Yang, J., Wu, F., & Liu, H. (2018). Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks. International Journal of Distributed Sensor Networks, 14(2), 1550147718761583.Google Scholar