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Intell."],"published-print":{"date-parts":[[2023,3,15]]},"abstract":"<jats:p>The purpose of the influence maximization problem is to determine a subset to maximize the number of affected users. This problem is very crucial for information dissemination in social networks. Most traditional influence maximization methods usually focus too heavily on the information diffusion model and randomly set influence parameters, resulting in inaccurate final outcomes. Driven by the recent criticisms of the diffusion model and the rapid development of representation learning, this paper proposes a representation learning method based on improved random walk for influence maximization (IRWIM) to maximize the influence spread. The IRWIM algorithm improves the traditional random walk and adopts multi-task neural network architecture to predict the propagation ability of nodes more accurately. Moreover, the greedy strategy is utilized to continuously optimize the marginal gain while retaining the theoretical guarantee. IRWIM is tested on four genuine datasets. Experimental results show that the accuracy of the proposed algorithm is superior to various competitive algorithms in the field of influence maximization.<\/jats:p>","DOI":"10.1142\/s0218001423510035","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T08:02:41Z","timestamp":1671004961000},"source":"Crossref","is-referenced-by-count":0,"title":["Representation Learning Method Based on Improved Random Walk for Influence Maximization"],"prefix":"10.1142","volume":"37","author":[{"given":"Yuying","family":"Liu","sequence":"first","affiliation":[{"name":"Shandong Province Key Laboratory of Wisdom Mine Information Technology, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, P. R. 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