Fei Hao
ABSTRACT
Cohesive subgraph is regarded as an important topological structure in social network analysis. It is critical to understand the organization behavior of users and supervise the users work well together in order to achieve goals within a social network. Therefore, cohesive subgraph mining is becoming a critical research issue for social network analysis. However, there exist many challenges for mining cohesive subgraphs from massive social networks due to its large scale property. Aiming to reduce the size of social networks for fast analytic, this paper formulates the problem on concept lattice factorization in a social network and further devises a factorization algorithm. Importantly, an efficient community detection approach based on concept lattice factors is then proposed considering the concept lattice factors can well characterize the topology skeleton of a given social network.
- Radim Belohlavek and Vilem Vychodil. 2010. Discovery of optimal factors in binary data via a novel method of matrix decomposition. J. Comput. System Sci. 76, 1 (2010), 3–20.Google Scholar
Digital Library
- Fei Hao, Geyong Min, Zheng Pei, Doo-Soon Park, and Laurence T Yang. 2017. K -Clique Community Detection in Social Networks Based on Formal Concept Analysis. IEEE Systems Journal 11, 1 (2017), 250–259.Google ScholarCross Ref
- Fei Hao, Guangyao Pang, Zheng Pei, Ke Yun Qin, Yu Zhang, and Xiaoming Wang. 2019. Virtual Machines Scheduling in Mobile Edge Computing: A Formal Concept Analysis Approach. IEEE Transactions on Sustainable Computing (2019).Google Scholar
- Fei Hao, Doo-Soon Park, Geyong Min, Young-Sik Jeong, and Jong-Hyuk Park. 2016. k-Cliques mining in dynamic social networks based on triadic formal concept analysis. Neurocomputing 209 (2016), 57–66.Google ScholarDigital Library
- Fei Hao, Zheng Pei, Doo-Soon Park, Laurence T Yang, Young-Sik Jeong, and Jong-Hyuk Park. 2017. Iceberg clique queries in large graphs. Neurocomputing 256 (2017), 101–110.Google ScholarDigital Library
- Fei Hao, Yixuan Yang, Beibei Pang, Neil Y Yen, and Doo-Soon Park. 2019. A fast algorithm on generating concept lattice for symmetry formal context constructed from social networks. Journal of Ambient Intelligence and Humanized Computing (2019), 1–8.Google Scholar
- Mohamed-Hamza Ibrahim, Rokia Missaoui, and Abir Messaoudi. 2018. Detecting communities in social networks using concept interestingness. In Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering. IBM Corp., 81–90.Google ScholarDigital Library
- Ales Keprt and Václav Snásel. 2004. Binary Factor Analysis with Help of Formal Concepts.. In CLA, Vol. 110. 90–101.Google Scholar
- Haithem Mezni and Taher Abdeljaoued. 2018. A cloud services recommendation system based on Fuzzy Formal Concept Analysis. Data & Knowledge Engineering 116 (2018), 100–123.Google ScholarDigital Library
- Václav Snášel, Zdenek Horák, and Ajith Abraham. 2008. Understanding social networks using formal concept analysis. In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 3. IEEE, 390–393.Google Scholar
- Yuan Sun, Shiwei Ye, Yi Sun, and Tsunehiko Kameda. 2015. Improved algorithms for exact and approximate Boolean matrix decomposition. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 1–10.Google ScholarCross Ref
- Qiang Wu, Yan Dong, Yu-Xi Wu, and Li-Ping Xie. 2019. Concept lattice factorization based 3D model retrieval of mechanical parts. Acta Automatica Sinica 45, 2 (2019), 407–419.Google Scholar
- Yingxiu Zhao, Jinhai Li, Wenqi Liu, and Weihua Xu. 2017. Cognitive concept learning from incomplete information. International Journal of Machine Learning and Cybernetics 8, 1 (2017), 159–170.Google ScholarCross Ref
- Caifeng Zou, Daqiang Zhang, Jiafu Wan, Mohammad Mehedi Hassan, and Jaime Lloret. 2015. Using concept lattice for personalized recommendation system design. IEEE Systems Journal 11, 1 (2015), 305–314.Google ScholarCross Ref
Index Terms
- Concept Lattice Factorization in Social Networks
Recommendations
Mining social networks and their visual semantics from social photos
WIMS '11: Proceedings of the International Conference on Web Intelligence, Mining and SemanticsWith the new possibilities in communication and information management, social networks and photos have received plenty of attention in the digital age. But there has been little research about the possibility that social photos carry some implicit ...
Leveraging social software for social networking and community development at events
C&T '09: Proceedings of the fourth international conference on Communities and technologiesProfessional networking is a primary goal of people attending conferences and events. Over the past year we have developed an online social networking and community tool for events, Pathable, to help attendees meet the right people. Pathable provides an ...
Building social capital with Facebook: Type of network, availability of other media, and social self-efficacy matter#
Highlights- Type of friends affects building social capital via Facebook and traditional media.
AbstractFindings about Facebook's effect on relationships are mixed, possibly due to lack of models that acknowledge differences across users, types of their friends, and use of competing media. To address this, we proposed and tested how ...
Comments