{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T01:52:31Z","timestamp":1790387551639,"version":"4.1.0"},"reference-count":73,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T00:00:00Z","timestamp":1581033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JST CREST","award":["PMJCR1687"],"award-info":[{"award-number":["PMJCR1687"]}]},{"name":"JSPS Grant-in-Aid for Scientific Research(B)","award":["17H01785"],"award-info":[{"award-number":["17H01785"]}]},{"name":"JSPS  Grant-in-Aid  for  Early-Career  Scientists","award":["19K20352"],"award-info":[{"award-number":["19K20352"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE)-based approaches. Specifically, merely minimizing the loss of VAE increases the deviation from its primary objective. Focusing on Variational Graph Autoencoder for Community Detection (VGAECD) we found that optimizing the loss using the stochastic gradient descent often leads to sub-optimal community structure especially when initialized poorly. We address this shortcoming by introducing a dual optimization procedure. This procedure aims to guide the optimization process and encourage learning of the primary objective. Additionally, we linearize the encoder to reduce the number of learning parameters. The outcome is a robust algorithm that outperforms its predecessor.<\/jats:p>","DOI":"10.3390\/e22020197","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T11:50:28Z","timestamp":1581076228000},"page":"197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6078-1636","authenticated-orcid":false,"given":"Jun Jin","family":"Choong","sequence":"first","affiliation":[{"name":"Department of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-7409","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsuyoshi","family":"Murata","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1086\/jar.33.4.3629752","article-title":"An Information Flow Model for Conflict and Fission in Small Groups","volume":"33","author":"Zachary","year":"1977","journal-title":"J. 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