Abstract
Complex relationships and restrictions on social networking sites are severe issues in social network data acquisition. Covering information of all users in social network and ensuring timeliness of data acquisition is of great significance. Therefore, it is critical to develop an efficient data acquisition strategy. In particular, smart deployment of monitoring points on social networks has a great impact on data acquisition efficiency. In this paper, we formulate the monitoring point deployment issue as a capacitated set cover problem (CSCP) and present a maximum monitoring contribution rate deployment algorithm (MMCRD). We further compare the proposed algorithm with random approximation deployment algorithm (RD) and maximum out-degree approximation deployment algorithm (MOD), using synthetic BA scale-free networks and real-world social network datasets derived from Facebook, Twitter and Weibo. The results show that our MMCRD algorithm is superior to the other two deployment algorithms, since our approach can monitor the entire social network users by monitoring at most 12% of users, and meanwhile, guarantee timeliness.
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Acknowledgements
This work is supported by the Science and Technology Program of Guangzhou, China (No. 201802010025), the Fundamental Research Funds for the Central Universities (No. 2017BQ024), the Natural Science Foundation of Guangdong Province (No. 2017A030310428) and the University Innovation and Entrepreneurship Education Fund Project of Guangzhou (No. 2019PT103). The authors also thank the editors and reviewers for their constructive editing and reviewing, respectively.
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Guo, Z., Wang, Z., Zhang, R. (2019). MMCRD: An Effective Algorithm for Deploying Monitoring Point on Social Network. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds) Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding. CCKS 2018. Communications in Computer and Information Science, vol 957. Springer, Singapore. https://doi.org/10.1007/978-981-13-3146-6_4
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DOI: https://doi.org/10.1007/978-981-13-3146-6_4
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