ArticleScience advances2017
The ground truth about metadata and community detection in networks.
Article in Science advances, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
- Mesoscale structures in signed networks.Science advances · 2026Article
- Higher-order trade-offs in hypergraph community detection.Science advances · 2026Article
- STR-GNN: stability-regularized graph neural networks for suppressing spurious temporal variations in dynamic community detection.Scientific reports · 2026Article
- Benchmarking overlapping community detection methods for applications in human connectomics.Network neuroscience (Cambridge, Mass.) · 2026Article
- Normalized mutual information is a biased measure for classification and community detection.Nature communications · 2025Article
- Random walk based snapshot clustering for detecting community dynamics in temporal networks.Scientific reports · 2025Article
- Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL).Entropy (Basel, Switzerland) · 2025Article
- Two antagonistic objectives for one multi-scale graph clustering framework.Scientific reports · 2025Article
- Measuring group fairness in community detection.PloS one · 2025Article
- Article
- Cell Type Differentiation Using Network Clustering Algorithms.bioRxiv : the preprint server for biology · 2024Article
- Network community detection via neural embeddings.Nature communications · 2024Article
- Hierarchical communities in the larvalProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Structure and inference in hypergraphs with node attributes.Nature communications · 2024Article
- node2vec2rank: Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking.bioRxiv : the preprint server for biology · 2024Article
- Community detection with Greedy Modularity disassembly strategy.Scientific reports · 2024Article
- Quantifying metadata relevance to network block structure using description length.Communications physics · 2024Article
- Article
- Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.PloS one · 2024Article
- Living on the edge: network neuroscience beyond nodes.Trends in cognitive sciences · 2023Review
21 more citing papers are in PubMed but not listed here.
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.
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