%0 Journal Article
	%A Gkolfo I. Smani and  Vasileios Megalooikonomou
	%D 2022
	%J International Journal of Humanities and Social Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 191, 2022
	%T Influence Maximization in Dynamic Social Networks and Graphs
	%U https://publications.waset.org/pdf/10012778
	%V 191
	%X Influence and influence diffusion have been studied extensively in social networks. However, most existing literature on this task are limited on static networks, ignoring the fact that the interactions between users change over time. In this paper, the problem of maximizing influence diffusion in dynamic social networks, i.e., the case of networks that change over time is studied. The DM algorithm is an extension of Matrix Influence (MATI) algorithm and solves the Influence Maximization (IM) problem in dynamic networks and is proposed under the Linear Threshold (LT) and Independent Cascade (IC) models. Experimental results show that our proposed algorithm achieves a diffusion performance better by 1.5 times than several state-of-the-art algorithms and comparable results in diffusion scale with the Greedy algorithm. Also, the proposed algorithm is 2.4 times faster than previous methods.
	%P 650 - 655