Critical risk node identification in enterprise resource planning heterogeneous knowledge graphs
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 .
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BibTeX
@article{chen2026critical,
title = {{Critical risk node identification in enterprise resource planning heterogeneous knowledge graphs}},
author = {Ruizhe Chen and Jiajun Chen},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73477-9},
url = {https://www.nature.com/articles/s41598-026-73477-9},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Critical risk node identification in enterprise resource planning heterogeneous knowledge graphs
AU - Ruizhe Chen
AU - Jiajun Chen
PY - 2026
DA - 2026/10/08
JO - Scientific Reports
DO - 10.1038/s41598-026-73477-9
UR - https://www.nature.com/articles/s41598-026-73477-9
AB - 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 .
SN - 2045-2322
ER -