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We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose T<jats:sc>en<\/jats:sc>GAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better.<\/jats:p>","DOI":"10.1007\/s10618-023-00947-3","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T13:02:34Z","timestamp":1692622954000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TenGAN: adversarially generating multiplex tensor graphs"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5813-2266","authenticated-orcid":false,"given":"William","family":"Shiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin A.","family":"Miller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tina","family":"Eliassi-Rad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evangelos E.","family":"Papalexakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Al-Sharoa E, Al-khassaweneh M, Aviyente S (2017) A tensor based framework for community detection in dynamic networks. 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