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Related Experiment Video

Updated: Jun 18, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Random graph models for directed acyclic networks.

Brian Karrer1, M E J Newman

  • 1Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

We introduce new random graph models for directed acyclic graphs (DAGs). Our models accurately predict real-world network connections, outperforming existing random graph approaches.

Related Experiment Videos

Last Updated: Jun 18, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Network science
  • Graph theory
  • Computational biology

Background:

  • Directed acyclic graphs (DAGs) are crucial for modeling complex systems like citation networks and food webs.
  • Existing random graph models often fail to capture the properties of real-world DAGs.

Purpose of the Study:

  • To propose novel random graph models specifically for directed acyclic graphs.
  • To evaluate the accuracy of these models against empirical network data.

Main Methods:

  • Development of two new random graph models for DAGs, analogous to traditional undirected models.
  • Calculation of key network properties, focusing on vertex connection probabilities.
  • Comparison of model predictions with data from real-world acyclic networks.

Main Results:

  • The proposed DAG models demonstrate a high degree of agreement with real-world network data.
  • Model predictions for connection probabilities align well with empirical measurements.
  • Performance surpasses that of other commonly used random graph models for DAGs.

Conclusions:

  • The new random graph models provide a more accurate representation of directed acyclic networks.
  • These models offer a valuable tool for analyzing and understanding complex acyclic systems.
  • The findings suggest a significant improvement in modeling real-world networks using random graph theory.