ArticlePloS one2012
Statistical inference for valued-edge networks: the generalized exponential random graph model.
Article in PloS one, 2012. 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
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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.
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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
18 citing papers in PubMed.
- Fine-Scale Landscape Genomics Show Asymmetric Patterns of Gene Flow for the Invasive MosquitoEcology and evolution · 2026Article
- Parameter Estimation Procedures for Exponential-family Random Graph Models on Count-valued Networks: A Comparative Simulation Study.Social networks · 2024Article
- A Bayesian multilevel model for populations of networks using exponential-family random graphs.Statistics and computing · 2024Article
- Worth the Weight: Conceptualizing and Measuring Strong Versus Weak Tie Homophily.Social networks · 2022Article
- Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics.Frontiers in physiology · 2022Article
- A network analysis of patient referrals in two district health systems in Tanzania.Health policy and planning · 2021Article
- Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.NeuroImage · 2021Article
- Testing biological network motif significance with exponential random graph models.Applied network science · 2021Article
- Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis.Technometrics : a journal of statistics for the physical, chemical, and engineering sciences · 2020Article
- A survey on exponential random graph models: an application perspective.PeerJ. Computer science · 2020Article
- Connectivity in fMRI: Blind Spots and Breakthroughs.IEEE transactions on medical imaging · 2018Article
- Multi-relational measurement for latent construct networks.Psychological methods · 2018Article
- Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure.Scientific reports · 2017Article
- Analyzing Policy Networks Using Valued Exponential Random Graph Models: Do Government-Sponsored Collaborative Groups Enhance Organizational Networks?Policy studies journal: the journal of the Policy Studies Organization · 2016Article
- A two-part mixed-effects modeling framework for analyzing whole-brain network data.NeuroImage · 2015Article
- Analyzing complex functional brain networks: Fusing statistics and network science to understand the brainStatistics surveys · 2013Article
- Reciprocity of weighted networks.Scientific reports · 2013Article
- Clarifying the use of aggregated exposures in multilevel models: self-included vs. self-excluded measures.PloS one · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis.
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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.