ArticleAlgorithms2017
Post-Processing Partitions to Identify Domains of Modularity Optimization.
Article in Algorithms, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Article
- The power of weak, transient interactions across biology: A paradigm of emergent behavior.Physica D. Nonlinear phenomena · 2023Article
- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
- Social Media Sentiment about COVID-19 Vaccination Predicts Vaccine Acceptance among Peruvian Social Media Users the Next Day.Vaccines · 2023Article
- A multiresolution framework for the analysis of community structure in international trade networks.Scientific reports · 2023Article
- Finite-state parameter space maps for pruning partitions in modularity-based community detection.Scientific reports · 2022Article
- Harmonizing functional connectivity reduces scanner effects in community detection.NeuroImage · 2022Article
- Constant community identification in million-scale networks.Social network analysis and mining · 2022Article
- Article
- Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations.NeuroImage · 2021Article
- Classification of Long Noncoding RNAs by k-mer Content.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Assessing the robustness of cluster solutions obtained from sparse count matrices.Psychological methods · 2019Article
- The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability.NeuroImage · 2019Article
- Comparing Shared Patient Networks Across Payers.Journal of general internal medicine · 2019Article
- Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections.The Journal of clinical investigation · 2019Article
- Transient crosslinking kinetics optimize gene cluster interactions.PLoS computational biology · 2019Article
- Functional classification of long non-coding RNAs by k-mer content.Nature genetics · 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
5 authors.
Funding
Abstract
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition-i.e., the parameter-space domain where it has the largest modularity relative to the input set-discarding partitions with empty domains to obtain the subset of partitions that are "admissible" candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.
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.