Fragment Path Complex Networks for Viral Genome Classification

Accurate classification of viral families is essential to understanding genomic diversity, but rapid evolution and heterogeneous genome structure complicate reliable assignment. Newly discovered viruses, particularly those from underrepresented ecosystems, expand the known diversity of viral genomes. This growing diversity poses challenges for classification methods that rely on similarity to existing references. We present Fragment Path Complex Networks (FPCN), a framework that represents viral genomes as sequences of ordered fragments at various scales. FPCN captures contextual sequence information within each fragment and uses path complexes to model complex relationships between neighboring fragments. We evaluated FPCN on well-established NCBI benchmarks, and FPCN achieved higher classification accuracy than competing methods. Further evaluations on newly released datasets revealed FPCN's ability to generalize to genomes that were not presented in the training. Together, these results position FPCN as an effective tool for classifying viral genomes within known families across benchmark datasets and newly released genomes.