@article {Elhachmi2022456,
	title = {Interactive Large-Scale Graph Visualization and Analysis for Social Networks},
	journal = {Lecture Notes in Networks and Systems},
	volume = {455 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {456-465},
	abstract = {Dramatic popularity and prevalence of online social networking services have involved an increase of members publicly who articulate mutual {\textquotedblleft}friendship{\textquotedblright} relations. Therefore, people have shown a great need to exchange information, share data, and adopt innovations in social networking applications. Data exchange has become the backbone of the economy on a global scale. In this paper, we investigate the Interactive Large-Scale Graph Visualization and Analysis of Social Networks that can process large-scale data. We have adopted a concrete large-scale corporate social networking site to realize this work; however, the approach can be easily generalized to other Social Networks. The problem is first stated as clearly as possible, and the social network is formulated as a joint optimization problem of graph selection. Then we have proposed some low-complexity search algorithms. The simulation results prove that our approach achieves good performance with a much lower computational complexity. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-02447-4_47},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130348236\&doi=10.1007\%2f978-3-031-02447-4_47\&partnerID=40\&md5=162443e9c9f4e03e533664cde1fb7e60},
	author = {Elhachmi, J. and Cheikhani, Y.}
}
