{
  "abstract": "Updating a graph by inserting or replacing nodes while preserving semantics and reusing existing structure is a recurring computational problem. In population genetics, this problem arises in the genotype representation graph (GRG), a directed acyclic graph that losslessly encodes phased genetic variation across hundreds of thousands of samples by sharing subgraph structure for individual mutations. In a GRG, each mutation's carrier set is implicitly encoded as the set of leaf nodes reachable from the node it is assigned to. Updating a mutation is therefore a structural editing problem, and current approaches remap mutations individually. This paper introduces a batched mutation-remapping algorithm that replaces independent reuse-aware traversals with a single shared reverse-topological pass, identifying reuse candidates for an entire batch at once. The pass propagates compact bit-parallel per-mutation state and uses an adaptive sparse/dense carrier set representation spanning rare-to-common variant densities. Batching is the memory-scalable complement to split-based parallelism, which instead replicates graph and traversal state per worker. Our remapping is evaluated on a controlled update workload and on end-to-end allele polarization, a bulk carrier set update that is common in population genetic analysis. Our approach is up to 10.5$\\times$ faster than independent remapping while preserving exact carrier-set semantics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.26488",
  "author_total": 4,
  "authors": [
    {
      "name": "Aaron Li"
    },
    {
      "name": "Yifan Li"
    },
    {
      "name": "Drew DeHaas"
    },
    {
      "name": "Giulia Guidi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6ra00j8pw4h0y.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.26488",
  "fulltext_url": null,
  "id": "6ra00j8pw4h0y",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.26488",
  "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.DS"
    ],
    [
      "arxiv",
      "q-bio.PE"
    ],
    [
      "openalex",
      "1707|Graph Theory and Algorithms"
    ]
  ],
  "openalex": "W7204596774",
  "pdf_url": "https://arxiv.org/pdf/2608.26488",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6ra00j8pw4h0y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:21Z",
      "native_id": "2608.26488",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.26488"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.PE",
      "subfield": "evolutionary-biology"
    }
  ],
  "title": "Cheaper by the Batch: Shared Traversal for Genotype Graph Editing",
  "title_text": "Cheaper by the Batch: Shared Traversal for Genotype Graph Editing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:21Z",
  "url": "https://research.draconitic.com/paper/6ra00j8pw4h0y",
  "venue": "arXiv",
  "versions": []
}