{
  "abstract": "Enterprise Resource Planning (ERP) systems record high-volume, multi-typed transactions that are difficult to audit with manually specified rules alone. Existing graph-based anomaly detection methods often simplify ERP data into homogeneous graphs or use attention mechanisms that do not explicitly model the temporal ordering of audit events. We propose AuditGain, a framework for constructing ERP heterogeneous knowledge graphs and identifying critical risk nodes with an Adaptive Graph Attention Network. The model combines three main components: type-specific feature extraction for ERP entities, LSTM-enhanced multi-head attention for temporally ordered neighborhoods, and a Diffeomorphic Graph-Adaptive Activation Function (DiGRAF) for graph-conditioned nonlinear transformations. A community-level memory module based on training-period spectral clustering is used to retain cross-period context. Experiments on three proprietary ERP audit datasets and two public fraud detection benchmarks (YelpChi and Amazon) show consistent gains over heterogeneous, temporal, and fraud-oriented graph baselines. On ERP-Audit-A, AuditGain outperforms TGN by 4.2 percentage points and the strongest recent temporal baseline in our comparison by 3.5 percentage points in F1-score. Because the ERP datasets cannot be released under non-disclosure agreements, we provide public benchmark experiments, configuration files, and a synthetic ERP graph generator to support independent checks of the evaluation pipeline. The source code is publicly available at https://github.com/Drake-crz/AuditGain .",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Ruizhe Chen"
    },
    {
      "name": "Jiajun Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4yrz0nwd36ej2.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73477-9",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-73477-9",
  "id": "4yrz0nwd36ej2",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73477-9",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
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    [
      "openalex",
      "1404|ERP Systems Implementation and Impact"
    ]
  ],
  "openalex": "W7221009168",
  "pdf_url": "https://www.nature.com/articles/s41598-026-73477-9.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4yrz0nwd36ej2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.1038/s41598-026-73477-9",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73477-9"
    },
    {
      "first_seen": "2026-10-30T13:45:59Z",
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      "url": "https://doi.org/10.1038/s41598-026-73477-9"
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  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Critical risk node identification in enterprise resource planning heterogeneous knowledge graphs",
  "title_text": "Critical risk node identification in enterprise resource planning heterogeneous knowledge graphs",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:59Z",
  "url": "https://research.draconitic.com/paper/4yrz0nwd36ej2",
  "venue": "Scientific Reports",
  "versions": []
}