An Influence maximization algorithm in social network using K-shell decomposition and community detection
The increasing use of services and different applications of social networks has led to a wide range of research and studies in the field of information technology and computer networks towards such networks. Creating a wide platform for advertising in social networks and attracting more customers i...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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International Academy of Ecology and Environmental Sciences
2020-03-01
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| Series: | Network Biology |
| Subjects: | |
| Online Access: | http://www.iaees.org/publications/journals/nb/articles/2020-10(1)/influence-maximization-algorithm-in-social-network.pdf |
| Summary: | The increasing use of services and different applications of social networks has led to a wide range of research and studies in the field of information technology and computer networks towards such networks. Creating a wide platform for advertising in social networks and attracting more customers in this way has created a variety of ways to maximize profits. Therefore, due to the high importance of the propagation speed and the extent of advertising, the issue of influence maximization is considered special. The influence maximization can be described as: determining a small set of nodes capable of operating large waterfalls of behavior that are spread across the network. In other words, selection of a set of K nodes from a social network is in such a way that the influence of the influence of the node in the network has maximum value. Due to the high sensitivity of the influence maximization process, in this study we try to reduce the strengths and problems of previous strategies in this field by decomposition K-shell and community detection based on SLPA algorithm. The proposed approach in this research is based on the recognition of community based on SLPA algorithm, to make a better result by flexible and optimizing the decision making in exploration and extraction of societies. In both methods, K-shell analysis and community detection are used to choose the more influential nodes, which are proportional to the graph of social networks. The proposed method is evaluated based on two fundamental criteria of execution time and number of active nodes, which have better efficiency and efficiency compared to previous methods. |
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| ISSN: | 2220-8879 2220-8879 |