Can Zero-Shot Prompting Classify Risky Bitcoin Transactions from Compact One-Hop Narratives? A Small-Scale Exploratory Study

Zhouting Mo; Jiayuan Chen

Can Zero-Shot Prompting Classify Risky Bitcoin Transactions from Compact One-Hop Narratives? A Small-Scale Exploratory Study

Authors

Zhouting Mo Computer Technology, Fudan University, Shanghai, China
Jiayuan Chen Electronic Information Engineering, Communication University of China, Beijing, China

Keywords:

financial graph analysis, zero-shot prompting, graph verbalization, illicit transaction detection

Abstract

This study examines whether a compact open-weight language model can distinguish illicit from licit Bitcoin transactions when anonymous graph attributes are converted into short natural-language summaries. The inquiry is deliberately narrow: one 0.49-billion-parameter instruction-tuned language model, one public benchmark, 40 class-balanced labeled transactions, and CPU-only inference without training. Four conditions isolate the effects of feature scope and prompting: local attributes with a direct decision, local plus one-hop aggregate attributes with a direct decision, the same one-hop evidence with a short rationale, and three-sample self-consistency. Direct prompting produced valid labels for every item but collapsed to the illicit class. Both direct conditions reached Macro-F1 0.333 and balanced accuracy 0.500, so the added neighborhood summary yielded no observed discrimination gain. Rationale prompting retained Macro-F1 0.333 while reversing the single-class tendency toward licit predictions. Self-consistency returned only 8 valid majority decisions out of 40 and fell to Macro-F1 0.098. Its mean token burden was 841.3 tokens per decision, compared with 175.9 for local direct prompting. These results do not establish the general limits of language-based transaction screening. They show that, under a reproducible low-resource protocol, compressed anonymous statistics and prompt elaboration were insufficient to elicit stable class-sensitive behavior from the selected compact language model.

References

Dinh, T., Zeng, Y., Zhang, R., Lin, Z., Gira, M., Rajput, S., Sohn, J.-Y., Papailiopoulos, D., & Lee, K. (2022). LIFT: Language-interfaced fine-tuning for non-language machine learning tasks. Advances in Neural Information Processing Systems, 35, 11763–11784.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199–22213.

Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., & Zhou, D. (2023). Self-consistency improves chain of thought reasoning in large language models. International Conference on Learning Representations.

Wang, H., Feng, S., He, T., Tan, Z., Han, X., & Tsvetkov, Y. (2023). Can language models solve graph problems in natural language? Advances in Neural Information Processing Systems, 36, 30840–30861.

Fatemi, B., Halcrow, J., & Perozzi, B. (2024). Talk like a graph: Encoding graphs for large language models. International Conference on Learning Representations.

Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., & Huang, C. (2024). GraphGPT: Graph instruction tuning for large language models. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 491–500). https://doi.org/10.1145/3626772.3657775

Tang, J., Zhang, Q., Li, Y., Chen, N., & Li, J. (2025). GraphArena: Evaluating and exploring large language models on graph computation. International Conference on Learning Representations.

Wang, D., Zuo, Y., Li, F., & Wu, J. (2024). LLMs as zero-shot graph learners: Alignment of GNN representations with LLM token embeddings. Advances in Neural Information Processing Systems, 37.

Huang, X., Yang, Y., Wang, Y., Wang, C., Zhang, Z., Xu, J., Chen, L., & Vazirgiannis, M. (2022). DGraph: A large-scale financial dataset for graph anomaly detection. Advances in Neural Information Processing Systems, 35, 22765–22777.

Tang, J., Hua, F., Gao, Z., Zhao, P., & Li, J. (2023). GADBench: Revisiting and benchmarking supervised graph anomaly detection. Advances in Neural Information Processing Systems, 36, 29628–29653.

Elmougy, Y., & Liu, L. (2023). Demystifying fraudulent transactions and illicit nodes in the Bitcoin network for financial forensics. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 3979–3990). https://doi.org/10.1145/3580305.3599803

Lu, M., Han, Z., Rao, S. X., Zhang, Z., Zhao, Y., Shan, Y., Raghunathan, R., Zhang, C., & Jiang, J. (2022). BRIGHT—Graph neural networks in real-time fraud detection. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 3342–3351). https://doi.org/10.1145/3511808.3557136

Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., & He, Q. (2021). Pick and choose: A GNN-based imbalanced learning approach for fraud detection. In Proceedings of the Web Conference 2021 (pp. 3168–3177). https://doi.org/10.1145/3442381.3449989

Huang, M., Liu, Y., Ao, X., Li, K., Chi, J., Feng, J., Yang, H., & He, Q. (2022). AUC-oriented graph neural network for fraud detection. In Proceedings of the ACM Web Conference 2022 (pp. 1311–1321). https://doi.org/10.1145/3485447.3512178

Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 315–324). https://doi.org/10.1145/3340531.3411903

Liu, Z., Dou, Y., Yu, P. S., Deng, Y., & Peng, H. (2020). Alleviating the inconsistency problem of applying graph neural network to fraud detection. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1569–1572). https://doi.org/10.1145/3397271.3401253

Shi, F., Cao, Y., Shang, Y., Zhou, Y., Zhou, C., & Wu, J. (2022). H2-FDetector: A GNN-based fraud detector with homophilic and heterophilic connections. In Proceedings of the ACM Web Conference 2022 (pp. 1486–1494). https://doi.org/10.1145/3485447.3512195

Li, X., Liu, S., Li, Z., Han, X., Shi, C., Hooi, B., Huang, H., & Cheng, X. (2020). FlowScope: Spotting money laundering based on graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4731–4738. https://doi.org/10.1609/aaai.v34i04.5906

Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., & Jiang, C. (2023). Anti-money laundering by group-aware deep graph learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12444–12457. https://doi.org/10.1109/TKDE.2023.3272396

Song, K., Dhraief, M. A., Xu, M., Cai, L., Chen, X., Mithal, A., & Chen, J. (2024). Identifying money laundering subgraphs on the blockchain. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 195–203). https://doi.org/10.1145/3677052.3698635

Blanuša, J., Cravero Baraja, M., Anghel, A., von Niederhäusern, L., Altman, E., Pozidis, H., & Atasu, K. (2024). Graph feature preprocessor: Real-time subgraph-based feature extraction for financial crime detection. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 222–230). https://doi.org/10.1145/3677052.3698674

Lin, J., Guo, X., Zhu, Y., Mitchell, S., Altman, E., & Shun, J. (2024). FraudGT: A simple, effective, and efficient graph transformer for financial fraud detection. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 292–300). https://doi.org/10.1145/3677052.3698648

Li, K., Yang, T., Zhou, M., Meng, J., Wang, S., Wu, Y., Tan, B., Song, H., Pan, L., Yu, F., Sheng, Z., & Tong, Y. (2024). SEFraud: Graph-based self-explainable fraud detection via interpretative mask learning. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5329–5338). https://doi.org/10.1145/3637528.3671534

Altman, E., Blanuša, J., von Niederhäusern, L., Egressy, B., Anghel, A., & Atasu, K. (2023). Realistic synthetic financial transactions for anti-money laundering models. Advances in Neural Information Processing Systems, 36, 29851–29874. https://doi.org/10.52202/075280-1300

Li, M., & Xu, S. (2026). Trustworthy artificial intelligence in financial decision-making: A systematic review of explainability, fairness, and accountability. Journal of Sustainability, Policy, and Practice, 2(3), 104-114.

Wu, C., & Pan, Z. (2024). An Integrated Graph Neural Network and Reinforcement Learning Framework for Intelligent Drug Discovery. Journal of Advanced Computing Systems, 4(6), 19-29.

Li, Y., Zhang, X., & Hao, C. (2026). Graph Neural Network-Based Cross-Market Risk Contagion Analysis between US Equity Sectors and Treasury Yields during the 2023 Regional Banking Stress. Journal of Sustainability, Policy, and Practice, 2(4), 154-168.

Chen, Z., & Wang, M. (2025). Evaluating the Quality of Large Language Model-Generated Explanations in Recommendation Tasks: A Multi-Dimensional Comparative Analysis. Journal of Global Engineering Review, 3(1), 54-63.

Rao, G., Trinh, T. K., Chen, Y., Shu, M., & Zheng, S. (2024). Jump prediction in systemically important financial institutions' CDS prices. Spectrum of Research, 4(2).

Kang, A. & Ma, X. (2025). AI-Based Pattern Recognition and Characteristic Analysis of Cross-Border Money Laundering Behaviors in Digital Currency Transactions. Pinnacle Academic Press Proceedings Series 5, 1-19

Wang, Z., & Kang, A. (2025). FTAFO: A Federated Transparent Adaptive Financial Optimizer for Reducing Third-Party Dependencies in Workflow Management. Journal of Science, Innovation & Social Impact, 1(1), 329-339.

Kang, A., Zhang, K., & Chen, Y. (2025, July). AI-Assisted Analysis of Policy Communication during Economic Crises: Correlations with Market Confidence and Recovery Outcomes. In 2025 International Forum on Computing and Intelligent Systems (pp. 159-173). Pinnacle Academic Press.

Li, Y. & Mo, Z. (2026). Does a Predicted 0.9 Mean 90 Percent? A Comparative Evaluation of Post-hoc Probability Calibration Methods for Default-Probability Scoring under Class Imbalance. Academia Nexus Journal 5 (2)

Wu, X., Li, Y., Liu, Y., Pang, S., Wang, L., Wu, C., & Huo, H. (2019, June). Jointly detecting and retrieving vehicles from road image sequences based on CNN. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 2530-2535). IEEE.

Wu, C., Guan, H., & Weng, H. (2024). Forecasting Hospital Resource Demand Using Gradient Boosting: An Operational Analytics Approach for Bed Allocation and Patient Flow Management. Journal of Computing Innovations and Applications, 2(1), 74-85.

Chen, Y. (2026). Comparing prompting strategies and backbone choices for LLM agents on tabular credit-card fraud detection: An empirical study. Artificial Intelligence and Machine Learning Review, 7(3), 1–11

Peng, Y. and Gao, S., 2026. An Empirical Comparison of XGBoost and LightGBM for Capital Expenditure Deviation Prediction in US FERC-Regulated Rate-Base Transmission Projects. Journal of Sustainability, Policy, and Practice, 2(4), pp.90-102.

Li, L., Li, Y., Wu, C., Dong, H., Jiang, P., & Wang, F. (2021, January). Detail fusion GAN: High-quality translation for unpaired images with GAN-based data augmentation. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 1731-1736). IEEE.

Li, Y., Zhang, X., & Hao, C. (2026). Graph Neural Network-Based Cross-Market Risk Contagion Analysis between US Equity Sectors and Treasury Yields during the 2023 Regional Banking Stress. 2026 2nd International Conference on Digital Intelligence and Computing

Chen, Y. (2026). Comparing Prompting Strategies and Backbone Choices for LLM Agents on Tabular Credit-Card Fraud Detection: An Empirical Study. Journal of Innovative Technologies 2 (1)

Long, L., & Hu, J. (2026). Multi-Objective Particle Swarm Optimization for Site Selection and Policy Subsidy Maximization of Foreign Renewable Energy Enterprises in the United States. Artificial Intelligence and Machine Learning Review, 7(2), 54-69.

Luo, C., & Wang, X. (2026). Latency-Throughput Tradeoffs of ONNX Runtime, TensorRT-LLM, vLLM, and Triton: An Empirical Comparison on 1B-3B Parameter LLM Inference. Spectrum of Research, 6(1).

Graph Neural Network-Based Cross-Market Risk Contagion Analysis between U.S. Equity Sectors and Treasury Yields during the 2023 Regional Banking Stress

Fu, H., Ming, Y., Jiang, Y., & Fan, C. (2018, August). Effective SVD-based deep network compression for automatic speech recognition. In International Workshop on Face and Facial Expression Recognition from Real World Videos (pp. 35-47). Cham: Springer International Publishing.

Z. Feng, D. Zhang, Y. Wang (2024). Intraday Liquidity Patterns and Their Implications for Market Risk Assessment: Evidence from Global Equity Markets. Artificial Intelligence and Machine Learning Review

Kang, A., Li, Z., & Meng, S. (2023). AI-Enhanced Risk Identification and Intelligence Sharing Framework for Anti-money laundering in Cross-Border Income Swap Transactions. Journal of Advanced Computing Systems 3 (5), 34-47

Wu, Z., Feng, Z., & Dong, B. (2024). Optimal feature selection for market risk assessment: A dimensional reduction approach in quantitative finance. Journal of Computing Innovations and Applications 2 (1), 20-31

Li, Y., Zhao, F., & Hu, J. (2026). Identifying Cross-Market Risk Contagion Amplifiers via Graph Attention Networks: Empirical Evidence from US Financial Stress Periods. Journal of Computing Innovations and Applications 4 (1), 164-175

Shang, W., Zhao, F., & Liu, M. (2026). A Comparative Study of Spatiotemporal Clustering and Classification Approaches for Security Incident Risk Assessment in UN Peacekeeping Operations. Journal of Sustainability, Policy, and Practice, 2(3), 1-11.

Yiyi, C. (2025). Federated learning-based framework for privacy-protected cross-border financial risk evaluation: Analyzing US-asia investment flows. Journal of Sustainability, Policy, and Practice, 1(4), 50-65

Wei, C., Ge, L. & Brooks, N. (2024). Graph-based representation learning for financial fraud and anomaly transaction detection. Journal of Computing Innovations and Applications 2 (1), 153-164

Li, Y. & Fu, X. (2026). Comparative Evaluation of Graph Neural Networks for Cross-Market Risk Contagion Path Identification in Multi-Layer Financial Networks. 2026 2nd International Conference on Artificial Intelligence and Advanced

Li, Y., Buchberger, B., Liu, Y., Capone, C., Adak, T., Ojha, S., ... & Baran, P. S. (2026). General Access to C-Glycosides via Redox-Neutral Radical Cross-Coupling of Glycohydrazides.

Wei, C. & Wu, C. (2024). Credit risk transmission mechanism and prevention strategies in supply chain finance: A core enterprise perspective. Artificial Intelligence and Machine Learning Review 5 (2), 101-115

Ming, Y., Fu, H., Jiang, Y., & Yu, H. (2020). Variational Bayesian Group-Level Sparsification for Knowledge Distillation. IEEE Access, 8, 126628-126636.

Rao, GL., Wang, Z., & Liang, J. (2025). Reinforcement Learning for Pattern Recognition in Cross-Border Financial Transaction Anomalies: A Behavioral Economics Approach to AML. Applied and Computational Engineering 142, 116-127

Zhang, D. & Wang, Y. (2025). AI-driven quality assessment and investment risk identification for carbon credit projects in developing countries. 2025 International Forum on Computing and Intelligent Systems, 76-92

Zhang, D. & Ma, X. (2025). Machine Learning-Based Credit Risk Assessment for Green Bonds: Climate Factor Integration and Default Prediction Analysis. Journal of Sustainability, Policy, and Practice 1 (2), 121-135

Li, Y. & Zhang, S. (2025). Machine Learning-Based Credit Risk Early Warning System for Small and Medium-Sized Financial Institutions: An Ensemble Learning Approach with Interpretable Risk Indicators. Journal of Science, Innovation & Social Impact 1 (1), 372-383

Li, Y., Wu, C., Li, L., Liu, Y., & Zhu, J. (2021). Caption generation from road images for traffic scene modeling. IEEE Transactions on intelligent transportation systems, 23(7), 7805-7816.

Chen, Y. & Tang, T. (2026). Evaluating Prompt Engineering Strategies for Few-Shot Cyber Threat Intelligence Entity and Relation Extraction from Multi-Source Reports. Journal of Science, Innovation & Social Impact 2 (2), 153-164

Trinh, TK. & Zhang, D. (2024). Algorithmic fairness in financial decision-making: Detection and mitigation of bias in credit scoring applications. Journal of Advanced Computing Systems 4 (2), 36-49

Cao, H., Hu, J., & Luo, C. (2025). Behavioural Feature Analysis for Anomalous Click Detection in Mobile Advertising Environments: Toward In-App Browser-Specific Detection. Journal of Computing Innovations and Applications, 3(2), 96-105.

Z. Feng, C. Ni, S. Zhou (2025). Option-Implied Information for Forward-Looking Market Risk Assessment: Evidence from Commodity Derivatives Markets. Spectrum of Research

Rao, GL., Lu, T., Yan, L., & Liu, Y. (2024). A Hybrid LSTM-KNN Framework for Detecting Market Microstructure Anomalies: Evidence from High-Frequency Jump Behaviors in Credit Default Swap Markets. Journal of Knowledge Learning and Science Technology

Rao, GL., Trinh, TK., Chen, Y., Shu, M., & Zheng, S. (2024). Jump Prediction in Systemically Important Financial Institutions' CDS Prices. Spectrum of Research 4 (2)

Li, Y. & Ling, Z. (2026). Real-Time Multi-Risk Early Warning for Community Banks: An Application of Ensemble Anomaly Detection and Explainable Artificial Intelligence. Journal of Advanced Computing Systems 6 (2), 15-27

Ge, L. & Rao, G. (2025). MultiStream-FinBERT: A Hybrid Deep Learning Framework for Corporate Financial Distress Prediction Integrating Accounting Metrics, Market Signals, and Textual Disclosures. Pinnacle Academic Press Proceedings Series 3, 107-122

Yuan, D. & Zhang, D. (2025). APAC-sensitive anomaly detection: Culturally-aware AI models for enhanced AML in US securities trading. 2025 International Conference on Computer, AI, Systems and Automation, 108-121

Li, Y. & Mo, Z. (2026). Backtesting Value-at-Risk and Expected Shortfall Across Calm and Turbulent Markets: An Empirical Comparison of Historical Simulation, GARCH-Family, Filtered Historical Simulation. Annals of Applied Sciences 7 (1)

Cao, H., & Liu, M. (2026). Empirical Evaluation of Constraint Discovery Techniques for Business Logic Consistency Verification in Payroll Data Migration. Journal of Sustainability, Policy, and Practice, 2(3), 92-103.

Li, Y. (2026). Enhancing Financial Compliance Transparency through Automated Data Governance and Intelligent Risk Reporting. Journal of Science, Innovation & Social Impact 2 (1), 299-313

Y. Li, S. Fan, H. Wang (2025). Machine Learning-Based Identification of Anomalous Trading Behavior Patterns Among Asia-Pacific Investors in U.S. Securities Markets. Spectrum of Research

Tang, T., & Yu, M. (2024). A Comparative Evaluation of LLM-Generated Semantic Tags versus Classical Text Features (TF-IDF, LDA, BERT Embeddings) for User-Interest Enrichment in Short-Video Recommendation. Artificial Intelligence and Machine Learning Review, 5(1), 129-140.

Jiang, Z., & Wang, M. (2025). Evaluation and Analysis of Chart Reasoning Accuracy in Multimodal Large Language Models: An Empirical Study on Influencing Factors. Pinnacle Academic Press Proceedings Series, 3, 43-58.

Liu, W., Rao, G., & Lian, H. (2023). Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Reinforcement Learning. Journal of Computing Innovations and Applications 1 (2), 47-58

Wu, C., Li, Y., Li, L., Wang, L., & Liu, Y. (2020, October). Caption Generation from Road Images for Traffic Scene Construction. In 2020 IEEE Intelligent Vehicles Symposium (IV) (pp. 1271-1276). IEEE.

Wu, Z., Zhang, Z., Zhao, Q., & Yan, L. (2025). Privacy-preserving financial transaction pattern recognition: A differential privacy approach. Preprints

Wang, M., & Zhu, L. (2024). Linguistic analysis of verb tense usage patterns in computer science paper abstracts. Academia Nexus Journal, 3(3).

Li, Y. & Fu, H. (2026). Interpretable Gradient Boosting for Transmission Capital Expenditure Deviation: A Multi-Decade Benchmark on FERC-Regulated Utilities. Academia Nexus Journal 5 (2)

Sun, M., & Wang, H. (2025). AI-powered customer engagement sequence analysis and conversion funnel optimization in multi-channel e-commerce marketing. Journal of Industrial Engineering and Applied Science, 3(5), 7-20

Ming, Y., & Fu, H. (2020, July). Variational bayesian sparsification for distillation compression. In 2020 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.

Cao, H. (2024). Privacy-Preserving Click Pattern Anomaly Detection for Mobile In-App Browser Advertising Fraud. COMPUTING INNOVATIONS AND APPLICATIONS 2 (2), 151-161

Does an LLM-Based Feedback Agent Move the Needle? An Empirical Study of Student Correctness and Hint Dependency on the ASSISTments 2017 Interaction Logs

Kang, A., Zhang, K., & Chen, Y. (2025). AI-Assisted Analysis of Policy Communication during Economic Crises: Correlations with Market Confidence and Recovery Outcomes. Pinnacle Academic Press Proceedings Series 3, 159-173

Yu, M., & Li, Z. (2025). An Empirical Comparison of Discrete Video Tokenization Schemes for Video Question Answering and Video Captioning. Artificial Intelligence and Machine Learning Review, 6(2), 27-50.

Dong, B., Zhang, D., & Xin, J. (2024). Deep reinforcement learning for optimizing order book imbalance-based high-frequency trading strategies. Journal of Computing Innovations and Applications 2 (2), 33-43

Kang, A., Xin, J., & Ma, X. (2024). Anomalous cross-border capital flow patterns and their implications for national economic security: An empirical analysis. Journal of Advanced Computing Systems 4 (5), 42-54

Kang, A. & Yu, K. (2025). The Impact of Financial Data Visualization Techniques on Enhancing Budget Transparency in Local Government Decision-Making. Spectrum of Research 5 (2)

Kang, A., Li, C., & Meng, S. (2025). The Impact of Government Budget Data Visualization on Public Financial Literacy and Civic Engagement. Journal of Economic Theory and Business Management 2 (4), 1-16

Wu, X., Li, Y., Hao, Z., Wu, C., Wang, X., & Liu, Y. (2019, November). Image style transformation based on structure GAN. In 2019 Chinese Automation Congress (CAC) (pp. 2002-2007). IEEE.

P. Li, W. Liu, Q. Zheng (2024). An Empirical Study on the Quality Assessment of Code Comments Generated by Large Language Models for Different Programming Paradigms. Spectrum of Research

Hu, J., Wang, X., & Lai, J. (2026). Benchmarking Learned Cardinality Estimation Techniques for Analytical Query Processing in Data Warehouses. Journal of Computer Technology and Applied Mathematics, 3(3), 1-8.

Guo, Y., Li, Y., Chen, S., Wu, Y., Poll, O., Ren, Z., ... & Baran, P. S. (2025). Scalable total synthesis of saxitoxin and related natural products. Nature, 646(8084), 351-357.

Chen, Y., & Hu, J. (2026). Graph Neural Network-Based Cascading Disruption Path Identification in Multi-Tier Rare Earth Processing Networks. Journal of Global Engineering Review, 4(1), 99-112.

Kang, A., Min, S., & Yuan, D. (2024). Comparative Analysis of Foreign Exchange Market Shock Transmission and Recovery Resilience Among Major Economies Under Geopolitical Conflicts: Evidence from the Russia-Ukraine Crisis. Journal of Computing Innovations and Applications, 2(1), 46-61.

Min, S. & Wei, C. (2023). Comparative analysis of filter-based feature selection methods for high-dimensional data in classification tasks. Journal of Advanced Computing Systems 3 (8), 25-38

Zhao, F., & Tang, T. (2026). AI-Based Sentiment Analysis for Stock Market Prediction: A Systematic Literature Review. Journal of Sustainability, Policy, and Practice, 2(3), 115-124.

Can Zero-Shot Prompting Classify Risky Bitcoin Transactions from Compact One-Hop Narratives? A Small-Scale Exploratory Study