Deep Learning Attention Model for Supervised and Unsupervised Network Community Detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00137485" target="_blank" >RIV/00216224:14310/23:00137485 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-36027-5_51" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-36027-5_51</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-36027-5_51" target="_blank" >10.1007/978-3-031-36027-5_51</a>

Result language
angličtina
Original language name
Deep Learning Attention Model for Supervised and Unsupervised Network Community Detection
Original language description
Network community detection is a complex problem that has to utilize heuristic approaches. It often relies on optimizing partition quality functions, such as modularity, description length, stochastic block-model likelihood etc. However, direct application of the traditional optimization methods has limited efficiency in finding the global maxima in such tasks. This paper proposes a novel bi-partite attention graph neural network model for supervised and unsupervised network community detection, suitable for unsupervised optimization of arbitrary partition quality functions, as well as for minimization of a loss function against the provided partition in a supervised setting. The model is demonstrated to be helpful in the unsupervised improvement of suboptimal partitions previously obtained by other known methods like Louvain algorithm for some of the classic and synthetic networks. It is also shown to be efficient in supervised learning of the provided community structure for a set of classic and synthetic networks. Furthermore, the paper serves as a proof-of-concept for the broader application of graph neural network models to unsupervised network optimization.
Czech name
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Czech description
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Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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