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The topology of real social network is large-scale, dynamic, and heterogeneous. The heterogeneity, and continuous expansion and evolution of social network pose a challenge to find influential users. Existing IM algorithms usually assume that social networks are static or dynamic but homogeneous to simplify the complexity of the IM problem. We propose a community-based influence maximization algorithm using network embedding in dynamic heterogeneous social networks. We use DyHATR algorithm to obtain the propagation feature vectors of network nodes, and execute<jats:italic>k<\/jats:italic>-means cluster algorithm to transform the original network into a coarse granularity network (CGN). On CGN, we propose a community-based three-hop independent cascade model and construct the objective function of IM problem. We design a greedy heuristics algorithm to solve the IM problem with<jats:inline-formula content-type=\"math\/tex\"><jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\((1-\\frac{1}{e})-\\)<\/jats:tex-math><\/jats:inline-formula>approximation guarantee and use community structure to quickly identify seed users and estimate their influence value. Experimental results on real social networks demonstrated that compared with existing IM algorithms, our proposed algorithm had better comprehensive performance with respect to the influence value, more less execution time and memory consumption, and better scalability.<\/jats:p>","DOI":"10.1145\/3594544","type":"journal-article","created":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T11:13:04Z","timestamp":1682766784000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Community-Based Influence Maximization Using Network Embedding in Dynamic Heterogeneous Social Networks"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1337-2466","authenticated-orcid":false,"given":"Xi","family":"Qin","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology and School of Computer, Electronics and Information, and the Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities, Guangxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-2151","authenticated-orcid":false,"given":"Cheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer, Electronics and Information, and the Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities, Guangxi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1653-4854","authenticated-orcid":false,"given":"Hai Xiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Delft Inst Appl Math, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,28]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"636","article-title":"On influential node discovery in dynamic social networks","author":"Aggarwal C.","year":"2012","unstructured":"C. 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