ArticleScientific reports2020
Community detection with node attributes in multilayer networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Article
- A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.Neuroinformatics · 2024Article
- Structure and inference in hypergraphs with node attributes.Nature communications · 2024Article
- Motif-based community detection in heterogeneous multilayer networks.Scientific reports · 2024Article
- Quantifying metadata relevance to network block structure using description length.Communications physics · 2024Article
- Community detection in large hypergraphs.Science advances · 2023Article
- Inference of hyperedges and overlapping communities in hypergraphs.Nature communications · 2022Article
- Community detection in networks by dynamical optimal transport formulation.Scientific reports · 2022Article
- Inferring functional communities from partially observed biological networks exploiting geometric topology and side information.Scientific reports · 2022Article
- The interplay between ranking and communities in networks.Scientific reports · 2022Article
- Social hierarchies and social networks in humans.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactions, thus generalizing standard methods to multilayer networks. Often, though, one can access additional information regarding individual nodes, attributes, or covariates. A relevant question is thus how to properly incorporate this extra information in such frameworks. Here we develop a method that incorporates both the topology of interactions and node attributes to extract communities in multilayer networks. We propose a principled probabilistic method that does not assume any a priori correlation structure between attributes and communities but rather infers this from data. This leads to an efficient algorithmic implementation that exploits the sparsity of the dataset and can be used to perform several inference tasks; we provide an open-source implementation of the code online. We demonstrate our method on both synthetic and real-world data and compare performance with methods that do not use any attribute information. We find that including node information helps in predicting missing links or attributes. It also leads to more interpretable community structures and allows the quantification of the impact of the node attributes given in input.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.