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- W4313483345 abstract "The increasing prevalence of multiplex networks has spurred a critical need to take into account potential dependencies across different layers, especially when the goal is community detection, which is a fundamental learning task in network analysis. We propose a full Bayesian mixture model for community detection in both single-layer and multi-layer networks. A key feature of our model is the joint modeling of the nodal attributes that often come with the network data as a spatial process over the latent space. In addition, our model for multi-layer networks allows layers to have different strengths of dependency in the unique latent position structure and assumes that the probability of a relation between two actors (in a layer) depends on the distances between their latent positions (multiplied by a layer-specific factor) and the difference between their nodal attributes. Under our prior specifications, the actors' positions in the latent space arise from a finite mixture of Gaussian distributions, each corresponding to a cluster. Simulated examples show that our model performs favorably compared to the existing ones. The model is also applied to a real three-layer network of employees in a law firm." @default.
- W4313483345 created "2023-01-06" @default.
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- W4313483345 date "2022-12-30" @default.
- W4313483345 modified "2023-09-26" @default.
- W4313483345 title "A Bayesian latent position approach for community detection in single- and multi-layer networks with continuous attributes" @default.
- W4313483345 doi "https://doi.org/10.48550/arxiv.2301.00055" @default.
- W4313483345 hasPublicationYear "2022" @default.
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