DataTrustGraph: Graph Learning for Dynamic Reliability Assessment of Enterprise Data
Keywords:
graph neural networks; data quality; dynamic graphs; enterprise data governance; data provenance; trust propagation; anomaly detection; knowledge graph embedding; graph attention networksAbstract
The trustworthiness of any given data record is becoming an integral part of the trustworthiness of the entities to which it is linked; as a result, the interconnected and evolving nature of data has become increasingly important to enterprise organizations. Traditional data quality assessment methods that are based on the periodic evaluation of data records, and that usually assess data quality at the record level, are not well suited to environments such as those where data is constantly ingested, records are dependent on each other across multiple systems, and adversarial manipulation is a possibility. This paper presents a synthesis of literature from graph anomaly detection, static and dynamic graph representation learning, knowledge graph embedding, data provenance management, and classical data quality theory, and proposes DataTrustGraph, which is a conceptual framework for continuous, graph-based, enterprise data reliability assessment. The framework represents enterprise entities, transformations and provenance events as a heterogeneous, evolving graph, on which reliability scores are propagated via attention-weighted neighborhood aggregation, similar to the propagation methods developed in graph attention and temporal graph networks. The synthesis is expressed as a composite reliability equation combining intrinsic, dimension-based data quality scoring with dynamically propagated neighborhood trust. The reviewed literature indicates that memory-based, inductive, and attention-based graph architectures are complementary in capturing the structural, semantic, and temporal aspects of data unreliability, and that the study of data provenance and data governance emphasizes the role of auditability and access control as essential, but often under-integrated, elements of trust computation. The paper concludes by identifying three directions for future research: incremental graph learning, explainable data quality, and agreed evaluation protocols.
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Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 29(3), 626–688. https://doi.org/10.1007/s10618-014-0365-y
Bellini, E., Iraqi, Y., & Damiani, E. (2020). Blockchain-based distributed trust and reputation management systems: A survey. IEEE Access, 8, 21127–21151. https://doi.org/10.1109/ACCESS.2020.2969820
Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Harmouch, H., & Naumann, F. (2022). The effects of data quality on machine learning performance. arXiv. https://doi.org/10.48550/arXiv.2207.14529
Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14, Article 2. https://doi.org/10.5334/dsj-2015-002
Cichy, C., & Rass, S. (2019). An overview of data quality frameworks. IEEE Access, 7, 24634–24648. https://doi.org/10.1109/ACCESS.2019.2899751
Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 1025–1035). Curran Associates.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations.
Kumar, S., Zhang, X., & Leskovec, J. (2019). Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1269–1278). Association for Computing Machinery. https://doi.org/10.1145/3292500.3330895
Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., Xiong, H., & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012–12038. https://doi.org/10.1109/TKDE.2021.3118815
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. arXiv. https://doi.org/10.48550/arXiv.2006.10637
Sankar, A., Wu, Y., Gou, L., Zhang, W., & Yang, H. (2020). DySAT: Deep neural representation learning on dynamic graphs via self-attention networks. In Proceedings of the 13th International Conference on Web Search and Data Mining (pp. 519–527). Association for Computing Machinery. https://doi.org/10.1145/3336191.3371845
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network model. IEEE Transactions on Neural Networks, 20(1), 61–80. https://doi.org/10.1109/TNN.2008.2005605
Scheliga, B., Markovic, M., Rowlands, H., Wozniak, A., Wilde, K., & Butler, J. (2022). Data provenance tracking and reporting in a high-security digital research environment. International Journal of Population Data Science, 7(3). https://doi.org/10.23889/ijpds.v7i3.1880
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations.
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017). Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29(12), 2724–2743. https://doi.org/10.1109/TKDE.2017.2754499
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous graph attention network. In The World Wide Web Conference (pp. 2022–2032). Association for Computing Machinery. https://doi.org/10.1145/3308558.3313562
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001
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