Network motif detection using hidden markov models.

ABSTRACT: 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 eng

SUBMITTER: Bampos C 

PROVIDER: S-EPMC12647810 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature