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Related Experiment Video

Updated: Jan 10, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Network motif detection using hidden markov models.

Costas Bampos¹, Vasileios Megalooikonomou²

¹Computer Engineering and Informatics Department, School of Engineering, University of Patras, Patras, Greece. costas.bampos@gmail.com.

Scientific Reports

|November 25, 2025

Summary

This summary is machine-generated.

This study introduces Hidden Markov Models (HMMs) for network motif detection, enabling accurate identification of recurring subgraph patterns in complex networks even with noisy data. The novel HMM approach offers a probabilistic framework for analyzing network structures.

Keywords:

Baum–Welch Hidden markov models Motif detection Viterbi

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Area of Science:

Computational Biology
Network Science
Machine Learning

Background:

Complex networks are modeled using vertices and edges, with recurring subgraphs (motifs) revealing organizational principles.
Existing methods for network motif detection often lack robustness to missing or noisy data.

Purpose of the Study:

To introduce a novel application of Hidden Markov Models (HMMs) for network motif detection.
To develop a probabilistic scoring framework for identifying network motifs that is tolerant to missing or noisy edges.

Main Methods:

Encoding subgraphs as short symbolic sequences.
Utilizing standard HMM kernels (Viterbi/Forward) for scoring sequences.
Applying the HMM pipeline to a 253-node directed benchmark network.

Main Results:

The HMM pipeline achieved accuracy comparable to exact enumeration for recovering known 4-node motifs.
The approach provides graded likelihoods, tolerating missing or noisy edges in network data.
A complexity comparison with existing tools (ESU, FANMOD, G-Tries) was performed.

Conclusions:

This work presents the first application of HMMs to network motif detection.
The developed HMM approach offers a practical, probabilistic, and weight-aware framework for network analysis.