ArticleNature communications2016
Structure and inference in annotated networks.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
58 citing papers in PubMed, 388 citations in OpenAlex.
- The Network Structure of Tobacco Withdrawal in a Community Sample of Smokers Treated With Nicotine Patch and Behavioral Counseling.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2020Trial
- Predicting missing links in food webs using stacked models and species traits.Nature communications · 2026Article
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- Hierarchical communities in the larvalProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Exploring animal behaviour multilayer networks in immersive environments - a conceptual framework.Journal of integrative bioinformatics · 2024Article
- Structure and inference in hypergraphs with node attributes.Nature communications · 2024Article
- Quantifying metadata relevance to network block structure using description length.Communications physics · 2024Article
- Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Towards a biologically annotated brain connectome.Nature reviews. Neuroscience · 2023Review
- Community detection in large hypergraphs.Science advances · 2023Article
- Prediction models with graph kernel regularization for network data.Journal of applied statistics · 2023Article
- EXTENDED STOCHASTIC BLOCK MODELS WITH APPLICATION TO CRIMINAL NETWORKS.The annals of applied statistics · 2022Article
- Inference of hyperedges and overlapping communities in hypergraphs.Nature communications · 2022Article
- Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks.Science advances · 2022Article
- Community detection in networks by dynamical optimal transport formulation.Scientific reports · 2022Article
- Testing for association in multiview network data.Biometrics · 2022Article
- Exploiting node metadata to predict interactions in bipartite networks using graph embedding and neural networks.Royal Society open science · 2022Article
- Inferring functional communities from partially observed biological networks exploiting geometric topology and side information.Scientific reports · 2022Article
- Community Partitioning over Feature-Rich Networks Using an Extended K-Means Method.Entropy (Basel, Switzerland) · 2022Article
- Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this 'metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.
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What OpenQuestion holds
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.