Graph neural network inspired algorithm for unsupervised network community detection
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | Applied Network Science |
| MU Faculty or unit | |
| Citation | |
| web | https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00500-z |
| Doi | http://dx.doi.org/10.1007/s41109-022-00500-z |
| Keywords | Complex networks; Community detection; Network science |
| Description | 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. |
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