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English Information

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Authors
# Name
1 Roberval Mariano(marianoroberval@yahoo.com.br)
2 Vania Vidal(vvidal@lia.ufc.br)

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Reference
# Reference
1 AKOGLU, Leman; TONG, Hanghang; KOUTRA, Danai. Graph-based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, v. 29, n. 3, p. 626–688, 2014. DOI: https://doi.org/10.1007/s10618-014-0365-y.
2 GAO, Yunfan et al. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv, 2023
3 HOGAN, Aidan et al. Knowledge graphs. ACM Computing Surveys, v. 54, n. 4, p. 1–37, 2021
4 LEWIS, Patrick et al. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS, 2020
5 PAN, Shirui et al. Large language models and knowledge graphs: opportunities and challenges. IEEE Transactions on Knowledge and Data Engineering, 2023
6 PENG, Boci et al. Graph Retrieval-Augmented Generation: A Survey. arXiv:2408.08921v1 [cs.AI] 15 Aug 2024
7 POURHABIB, Amir et al. Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 2020.
8 VIDAL, Vânia Maria Ponte et al. A Conceptual Framework for Building and Exploring Semantic Views of Enterprise Knowledge Graphs. Journal of Information and Data Management (JIDM), 2026
9 WEBER, Mark et al. Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. KDD, 2019.