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<p>Graphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs—or motifs—revealing fundamental organizational principles. We present a novel application of Hidden Markov Models (HMMs) to network motif detection: subgraphs are encoded as short symbolic sequences and scored with standard HMM kernels (Viterbi/Forward; optional Baum–Welch), producing graded likelihoods that tolerate missing or noisy edges. On a 253-node directed benchmark the HMM pipeline recovers known 4-node motifs with accuracy comparable to exact enumeration while providing a probabilistic, weight-aware scoring framework that enables principled model comparison. We also include a concise complexity comparison with ESU, FANMOD and G-Tries and discuss engineering choices (seed-and-filter, scoring-only workflows) that make the approach practical. To our knowledge, this is the first application of HMMs to network motif detection.</p>

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Network motif detection using hidden markov models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Costas Bampos,
  • Vasileios Megalooikonomou

摘要

Graphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs—or motifs—revealing fundamental organizational principles. We present a novel application of Hidden Markov Models (HMMs) to network motif detection: subgraphs are encoded as short symbolic sequences and scored with standard HMM kernels (Viterbi/Forward; optional Baum–Welch), producing graded likelihoods that tolerate missing or noisy edges. On a 253-node directed benchmark the HMM pipeline recovers known 4-node motifs with accuracy comparable to exact enumeration while providing a probabilistic, weight-aware scoring framework that enables principled model comparison. We also include a concise complexity comparison with ESU, FANMOD and G-Tries and discuss engineering choices (seed-and-filter, scoring-only workflows) that make the approach practical. To our knowledge, this is the first application of HMMs to network motif detection.