Wenlong Zhu^1,*, Yu Miao¹, Shuangshuang Yang², Zuozheng Lian¹, Lianhe Cui¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3111-3131, 2023, DOI:10.32604/cmc.2023.036159

Abstract Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic of nodes by improving… More >

Sonia¹, Kapil Sharma^1,*, Monika Bajaj²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1087-1101, 2023, DOI:10.32604/iasc.2023.026134

Abstract Big Data and artificial intelligence are used to transform businesses. Social networking sites have given a new dimension to online data. Social media platforms help gather massive amounts of data to reach a wide variety of customers using influence maximization technique for innovative ideas, products and services. This paper aims to develop a deep learning method that can identify the influential users in a network. This method combines the various aspects of a user into a single graph. In a social network, the most influential user is the most trusted user. These significant users are used for viral marketing as… More >

Senbo Chen^{1, 3, *}, Wenan Tan^{1, 2}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 345-358, 2020, DOI:10.32604/cmc.2020.09807

Abstract The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest influence. Up to now, most of the research has tended to focus on monolayer network rather than on multiplex networks. But in the real world, most individuals usually exist in multiplex networks. Multiplex networks are substantially different as compared with those of a monolayer network. In this paper, we integrate the multi-relationship of agents in multiplex networks by considering the existing and relevant… More >

Yong Hua¹,Bolun Chen^1,2,∗,Yan Yuan¹, Guochang Zhu¹, Fenfen Li¹

Journal on Internet of Things, Vol.1, No.2, pp. 77-88, 2019, DOI:10.32604/jiot.2019.05941

Abstract The problem of influence maximization in the social network G is to find k seed nodes with the maximum influence. The seed set S has a wider range of influence in the social network G than other same-size node sets. The influence of a node is usually established by using the IC model (Independent Cascade model) with a considerable amount of Monte Carlo simulations used to approximate the influence of the node. In addition, an approximate effect (1-1/e) is obtained, when the number of Monte Carlo simulations is 10000 and the probability of propagation is very small. In this paper,… More >

Yong Hua¹, Bolun Chen^1,2,*, Yan Yuan¹, Guochang Zhu¹, Jialin Ma¹

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 517-531, 2019, DOI:10.32604/cmc.2019.05278

Abstract The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network. Therefore, the comprehensive influence of node needs to be considered, when we choose the most influential node set consisted of k seed nodes. On account of the traditional methods used to measure the influence of nodes, such as degree centrality, betweenness centrality and closeness centrality, consider only a single aspect of the influence of node, so the influence measured by traditional methods mentioned above of node is not accurate. In this paper, we obtain the following result through experimental… More >