Graph neural network inspired algorithm for unsupervised network community detection

SOBOLEVSKY, Stanislav and Aliaksandr BELY. Graph neural network inspired algorithm for unsupervised network community detection. Applied Network Science. Springer Nature, 2022, vol. 7, No 1, p. 1-19. ISSN 2364-8228. Available from: https://dx.doi.org/10.1007/s41109-022-00500-z.

Basic information

• Original name: Graph neural network inspired algorithm for unsupervised network community detection
• Authors: SOBOLEVSKY, Stanislav (112 Belarus, guarantor, belonging to the institution) and Aliaksandr BELY (112 Belarus, belonging to the institution).
• Edition: Applied Network Science, Springer Nature, 2022, 2364-8228.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10200 1.2 Computer and information sciences
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 2.200
• RIV identification code: RIV/00216224:14310/22:00127675
• Organization unit: Faculty of Science
• Doi: http://dx.doi.org/10.1007/s41109-022-00500-z
• UT WoS: 000850086300002
• Keywords in English: Complex networks; Community detection; Network science
• Tags: rivok
• Tags: International impact, Reviewed

Abstract

Network community detection often relies on optimizing partition quality functions, like modularity. This optimization appears to be a complex problem traditionally relying on discrete heuristics. And although the problem could be reformulated as continuous optimization, direct application of the standard optimization methods has limited efficiency in overcoming the numerous local extrema. However, the rise of deep learning and its applications to graphs offers new opportunities. And while graph neural networks have been used for supervised and unsupervised learning on networks, their application to modularity optimization has not been explored yet. This paper proposes a new variant of the recurrent graph neural network algorithm for unsupervised network community detection through modularity optimization. The new algorithm’s performance is compared against the state-of-the-art methods. The approach also serves as a proof-of-concept for the broader application of recurrent graph neural networks to unsupervised network optimization.

Links

• EF16_019/0000822, research and development project: Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur
• MUNI/J/0008/2021, interní kód MU: Name: Digital City

Investor: Masaryk University, MASH JUNIOR - MUNI Award In Science and Humanities JUNIOR