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Updated: May 7, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Motifs in triadic random graphs based on Steiner triple systems.

Marco Winkler¹, Jörg Reichardt

¹Institute for Theoretical Physics, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 17, 2013

Summary

This study introduces novel generative models for complex networks using exponential random graph models (ERGMs) and Steiner triple systems (STSs). These models generate networks with specific subgraph motif patterns, aiding in understanding network function and structure.

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Last Updated: May 7, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

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Published on: March 18, 2019

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Published on: February 15, 2016

Area of Science:

Network science
Graph theory
Computational biology

Background:

Complex networks are conventionally viewed as built from pairwise links.
Subnetwork patterns, or motifs, are increasingly recognized as fundamental network building blocks.
Existing generative models lack the capability to test the functional roles of subgraph motifs.

Purpose of the Study:

To develop sound generative models for complex networks based on triadic substructures.
To address the challenge of independent specification of triad patterns in network models.
To enable the investigation of the functional implications of motif statistics.

Main Methods:

Utilizing exponential random graph models (ERGMs) framework.
Employing Steiner triple systems (STSs) to define independent triad specifications.
Combining ERGMs and STSs to create generative models for network ensembles.

Main Results:

Generated networks with non-trivial triadic Z-score profiles.
Identified inherent statistical correlations between triad pattern abundances.
Analytically calculated degree distributions for the generated triadic random graphs.

Conclusions:

The developed models provide a new tool for studying the functional significance of network motifs.
Understanding statistical correlations in motif abundance is crucial for interpreting network properties.
The findings advance the field of network science by offering a method to generate and analyze networks based on subgraph structures.