ArticleApplied network science2017
The many facets of community detection in complex networks.
Article in Applied network science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Unifying network connectivity from geodesics to random walks via the random cluster model.Nature communications · 2026Article
- Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL).Entropy (Basel, Switzerland) · 2025Article
- Networks as tools for defining emergent properties of microbiomes and their stability.Microbiome · 2024Review
- Finding multifaceted communities in multiplex networks.Scientific reports · 2024Article
- Robust, scalable, and informative clustering for diverse biological networks.Genome biology · 2023Article
- LazyFox: fast and parallelized overlapping community detection in large graphs.PeerJ. Computer science · 2023Article
- Statistical inference links data and theory in network science.Nature communications · 2022Review
- Detecting overlapping communities in complex networks using non-cooperative games.Scientific reports · 2022Article
- Activity of vehicles in the bus rapid transit system Metrobús in Mexico City.Scientific reports · 2022Article
- Principles and open questions in functional brain network reconstruction.Human brain mapping · 2021Review
- Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change.Scientific reports · 2021Article
- Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition.Scientific reports · 2021Article
- Embedding-based Silhouette community detection.Machine learning · 2020Article
- Mapping the community structure of the rat cerebral cortex with weighted stochastic block modeling.Brain structure & function · 2020Article
- Reduced network extremal ensemble learning (RenEEL) scheme for community detection in complex networks.Scientific reports · 2019Article
- From free text to clusters of content in health records: an unsupervised graph partitioning approach.Applied network science · 2019Article
- Weighted Stochastic Block Models of the Human Connectome across the Life Span.Scientific reports · 2018Article
- Convex skeletons of complex networks.Journal of the Royal Society, Interface · 2018Article
- Multiscale mixing patterns in networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Multiresolution Consensus Clustering in Networks.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.
Indexed as
Identifiers
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.