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DeepWalk Based Influence Maximization (DWIM): Influence Maximization Using Deep Learning

Sonia1, Kapil Sharma1,*, Monika Bajaj2

1 Department of Information Technology, Delhi Technological University, New Delhi, 110039, India
2 Department of Computer Science, University of Delhi, New Delhi, 110007, India

* Corresponding Author: Kapil Sharma. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 1087-1101.


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 the seeds to influence other users in the network. The proposed method combines both topical and topological aspects of a user in the network using collaborative filtering. The proposed method is DeepWalk based Influence Maximization (DWIM). The proposed method was able to find k influential nodes with computable time using the algorithm. The experiments are performed to assess the proposed algorithm, and centrality measures are used to compare the results. The results reveal its performance that the proposed method can find k influential nodes in computable time. DWIM can identify influential users, which helps viral marketing, outlier detection, and recommendations for different products and services. After applying the proposed methodology, the set of seed nodes gives maximum influence measured with respect to different centrality measures in an increased computable time.


Cite This Article

. Sonia, K. Sharma and M. Bajaj, "Deepwalk based influence maximization (dwim): influence maximization using deep learning," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 1087–1101, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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