{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:30:20Z","timestamp":1760236220674,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Defense Industrial Technology Development Program","award":["JCKY2018603B006"],"award-info":[{"award-number":["JCKY2018603B006"]}]},{"DOI":"10.13039\/501100019791","name":"CAEP Foundation","doi-asserted-by":"publisher","award":["CX2019040"],"award-info":[{"award-number":["CX2019040"]}],"id":[{"id":"10.13039\/501100019791","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users\u2019 social statuses and roles. However, this cannot fully reflect the overall characteristics of users\u2019 social statuses and roles in a social network. In this paper, we consider what social network structures reflect users\u2019 social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users\u2019 dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users\u2019 social statuses and roles in social networks through the use of an attention and gate mechanism on users\u2019 neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.<\/jats:p>","DOI":"10.3390\/e23111453","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:08Z","timestamp":1635805268000},"page":"1453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inferring Users\u2019 Social Roles with a Multi-Level Graph Neural Network Model"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5612-6516","authenticated-orcid":false,"given":"Chunrui","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China"},{"name":"Institution of Computer Application, China Academy of Engineering Physics, Mianyang 621900, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shen","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dechen","family":"Zhan","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyong","family":"Yin","sequence":"additional","affiliation":[{"name":"Institution of Computer Application, China Academy of Engineering Physics, Mianyang 621900, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Lou","sequence":"additional","affiliation":[{"name":"Institution of Computer Application, China Academy of Engineering Physics, Mianyang 621900, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1613\/jair.2229","article-title":"Topic and role discovery in social networks with experiments on enron and academic email","volume":"30","author":"McCallum","year":"2007","journal-title":"J. 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