{
  "abstract": "Complex networks are often compared using selected graph-theoretical measures that capture a selected set of properties with effects ranging from local to global, such as degree, clustering or betweenness centrality. Here we introduce a structural fingerprinting framework based on graphlets: small rooted subgraphs whose distributions provide a systematic description of local-to-mesoscale topology. Across synthetic networks generated from several random graph models, graphlet fingerprints capture parameter-dependent structural differences, outperform standard graph-theoretical measures, and identify even subtle local patterns driving discrimination. We then apply the framework to empirical resting-state functional connectomes, documenting that while graphlets show superior sensitivity also to controlled topological perturbations of brain connectivity, specifically in schizophrenia-control classification they perform only comparably to classical graph-theoretical features. This is in line with the notion that schizophrenia-related alterations are dominated by spatially localized connectivity changes rather than general topological reorganization. Altogether, the generative modeling, targeted perturbations and real-world neuroimaging classification challenge position graphlets as flexible structural fingerprints of complex networks, while carefully outlining their strength and weaknesses compared to more classical graph theoretical features.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.17445",
  "author_total": 5,
  "authors": [
    {
      "name": "Anna Pidnebesna"
    },
    {
      "name": "David Hartman"
    },
    {
      "name": "Aneta Pokorna"
    },
    {
      "name": "Daniel Trlifaj"
    },
    {
      "name": "Jaroslav Hlinka"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7naha6sn65rc5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.17445",
  "fulltext_url": null,
  "id": "7naha6sn65rc5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.17445",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.SI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "3109|Complex Network Analysis Techniques"
    ]
  ],
  "openalex": "W7213415483",
  "pdf_url": "https://arxiv.org/pdf/2609.17445",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7naha6sn65rc5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:46Z",
      "native_id": "2609.17445",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.17445"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Graphlets as structural fingerprints of complex networks",
  "title_text": "Graphlets as structural fingerprints of complex networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:46Z",
  "url": "https://research.draconitic.com/paper/7naha6sn65rc5",
  "venue": "arXiv",
  "versions": []
}