Publication details
Deep Learning Attention Model for Supervised and Unsupervised Network Community Detection
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Proceedings |
| Conference | Computational Science – ICCS 2023 |
| MU Faculty or unit | |
| Citation | |
| web | https://link.springer.com/chapter/10.1007/978-3-031-36027-5_51 |
| Doi | http://dx.doi.org/10.1007/978-3-031-36027-5_51 |
| Keywords | Complex networks; Community detection; Deep Learning; Graph Neural Networks |
| 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. |
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