{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:19:09Z","timestamp":1783109949350,"version":"3.54.6"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2023208001"],"award-info":[{"award-number":["F2023208001"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116353","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:28:15Z","timestamp":1780356495000},"page":"116353","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Vul-CGBT: Enhancing code vulnerability detection via contrastive semantic learning and graph embedding"],"prefix":"10.1016","volume":"347","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8641-2660","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YiFan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116353_b1","series-title":"National vulnerability database","year":"2024"},{"key":"10.1016\/j.knosys.2026.116353_b2","series-title":"2022 Open source security and risk analysis report","year":"2022"},{"key":"10.1016\/j.knosys.2026.116353_b3","unstructured":"Infer, [Online]. Available: https:\/\/fbinfer.com\/."},{"key":"10.1016\/j.knosys.2026.116353_b4","author":"Flawfinder","year":"2022"},{"key":"10.1016\/j.knosys.2026.116353_b5","author":"Checkmarx","year":"2022"},{"issue":"6","key":"10.1016\/j.knosys.2026.116353_b6","first-page":"4330","article-title":"Software vulnerability analysis method based on adaptive-k sequence clustering","volume":"12","author":"Wu","year":"2014","journal-title":"TELKOMNIKA Indones. J. Electr. Eng."},{"issue":"4","key":"10.1016\/j.knosys.2026.116353_b7","first-page":"1065","article-title":"A method for detecting software vulnerabilities based on clustering and model analyzing","volume":"7","author":"Ren","year":"2011","journal-title":"J. Comput. Inf. Syst."},{"key":"10.1016\/j.knosys.2026.116353_b8","doi-asserted-by":"crossref","unstructured":"H. Perl, S. Dechand, M. Smith, L. Williams, Vccfinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits, in: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, CCS, 2015, pp. 426\u2013437.","DOI":"10.1145\/2810103.2813604"},{"key":"10.1016\/j.knosys.2026.116353_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110830","article-title":"A user-centred evaluation of DisCERN: Discovering counterfactuals for code vulnerability detection and correction","volume":"278","author":"Wijekoon","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116353_b10","doi-asserted-by":"crossref","unstructured":"Z. Li, D. Zou, S. Xu, X. Ou, H. Jin, S. Wang, Z. Deng, Y. Zhong, VulDeePecker: A Deep Learning-Based System for Vulnerability Detection, in: Proceedings of the 25th Annual Network and Distributed System Security Symposium, NDSS, 2018.","DOI":"10.14722\/ndss.2018.23158"},{"issue":"4","key":"10.1016\/j.knosys.2026.116353_b11","doi-asserted-by":"crossref","first-page":"2244","DOI":"10.1109\/TDSC.2021.3051525","article-title":"SySeVr: A framework for using deep learning to detect software vulnerabilities","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Trans. Dependable Secur. Comput. (TDSC)"},{"issue":"3","key":"10.1016\/j.knosys.2026.116353_b12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3436877","article-title":"DeepWukong: Statically detecting software vulnerabilities using deep graph neural network","volume":"30","author":"Cheng","year":"2021","journal-title":"ACM Trans. Softw. Eng. Methodol. (TOSEM)"},{"key":"10.1016\/j.knosys.2026.116353_b13","doi-asserted-by":"crossref","unstructured":"Y. Wu, D. Zou, S. Dou, S. Xu, H. Jin, VulCNN: An Image-Inspired Scalable Vulnerability Detection System, in: Proceedings of the 44th International Conference on Software Engineering, ICSE, 2022, pp. 2365\u20132376.","DOI":"10.1145\/3510003.3510229"},{"key":"10.1016\/j.knosys.2026.116353_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2023.103501","article-title":"VulGAI: Vulnerability detection based on graphs and images","volume":"135","author":"Zhang","year":"2023","journal-title":"Comput. Secur."},{"key":"10.1016\/j.knosys.2026.116353_b15","article-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks","volume":"32","author":"Zhou","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"issue":"9","key":"10.1016\/j.knosys.2026.116353_b16","doi-asserted-by":"crossref","first-page":"3280","DOI":"10.1109\/TSE.2021.3087402","article-title":"Deep learning based vulnerability detection: Are we there yet?","volume":"48","author":"Chakraborty","year":"2021","journal-title":"IEEE Trans. Softw. Eng. (TSE)"},{"key":"10.1016\/j.knosys.2026.116353_b17","article-title":"CSGVD: A deep learning approach combining sequence and graph embedding for source code vulnerability detection","volume":"199","author":"Tang","year":"2023","journal-title":"J. Syst. Softw. (JSS)"},{"key":"10.1016\/j.knosys.2026.116353_b18","series-title":"Exploring software naturalness through neural language models","author":"Buratti","year":"2020"},{"key":"10.1016\/j.knosys.2026.116353_b19","series-title":"BERT: A review of applications in natural language processing and understanding","author":"Koroteev","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b20","series-title":"Roberta: A robustly optimized BERT pre-training approach","author":"Liu","year":"2019"},{"key":"10.1016\/j.knosys.2026.116353_b21","series-title":"Codebert: A pre-trained model for programming and natural languages","author":"Feng","year":"2020"},{"key":"10.1016\/j.knosys.2026.116353_b22","series-title":"Graphcodebert: Pre-training code representations with data flow","author":"Guo","year":"2020"},{"key":"10.1016\/j.knosys.2026.116353_b23","series-title":"Unified pre-training for program understanding and generation","author":"Ahmad","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b24","doi-asserted-by":"crossref","unstructured":"Z. Liu, Z. Tang, J. Zhang, Y. Wang, J. Li, M. Sun, Pre-Training by Predicting Program Dependencies for Vulnerability Analysis Tasks, in: Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering, ICSE, 2024, pp. 1\u201313.","DOI":"10.1145\/3597503.3639142"},{"key":"10.1016\/j.knosys.2026.116353_b25","series-title":"Unixcoder: Unified cross-modal pre-training for code representation","author":"Guo","year":"2022"},{"key":"10.1016\/j.knosys.2026.116353_b26","series-title":"Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation","author":"Wang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b27","doi-asserted-by":"crossref","unstructured":"S. Liu, B. Wu, X. Ye, J. Sun, X. Liu, Contrabert: Enhancing Code Pre-Trained Models via Contrastive Learning, in: 2023 IEEE\/ACM 45th International Conference on Software Engineering, ICSE, 2023, pp. 2476\u20132487.","DOI":"10.1109\/ICSE48619.2023.00207"},{"key":"10.1016\/j.knosys.2026.116353_b28","series-title":"Contrastive code representation learning","author":"Jain","year":"2020"},{"key":"10.1016\/j.knosys.2026.116353_b29","doi-asserted-by":"crossref","unstructured":"X. Li, Y. Gong, Y. Shen, X. Liu, D. Jiang, M. Sun, Coderetriever: A Large Scale Contrastive Pre-Training Method for Code Search, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2022, pp. 2898\u20132910.","DOI":"10.18653\/v1\/2022.emnlp-main.187"},{"key":"10.1016\/j.knosys.2026.116353_b30","doi-asserted-by":"crossref","unstructured":"H. Hanif, S. Maffeis, VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection, in: 2022 International Joint Conference on Neural Networks, IJCNN, 2022, pp. 1\u20138.","DOI":"10.1109\/IJCNN55064.2022.9892280"},{"issue":"8","key":"10.1016\/j.knosys.2026.116353_b31","doi-asserted-by":"crossref","first-page":"4196","DOI":"10.1109\/TSE.2023.3286586","article-title":"Vulnerability detection by learning from syntax-based execution paths of code","volume":"49","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Softw. Eng. (TSE)"},{"key":"10.1016\/j.knosys.2026.116353_b32","doi-asserted-by":"crossref","unstructured":"Z. Jiang, W. Sun, X. Gu, L. Chen, Y. Liu, Dfept: Data Flow Embedding for Enhancing Pre-Trained Model Based Vulnerability Detection, in: Proceedings of the 15th Asia-Pacific Symposium on Internetware, 2024, pp. 95\u2013104.","DOI":"10.1145\/3671016.3671388"},{"key":"10.1016\/j.knosys.2026.116353_b33","doi-asserted-by":"crossref","unstructured":"M. Fu, C. Tantithamthavorn, Linevul: A Transformer-Based Line-Level Vulnerability Prediction, in: Proceedings of the 19th International Conference on Mining Software Repositories, MSR, 2022, pp. 608\u2013620.","DOI":"10.1145\/3524842.3528452"},{"key":"10.1016\/j.knosys.2026.116353_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114406","article-title":"Automated vulnerability score prediction through lightweight generative AI","volume":"329","author":"Mirtaheri","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116353_b35","series-title":"Chain-of-thought prompting of large language models for discovering and fixing software vulnerabilities","author":"Nong","year":"2024"},{"key":"10.1016\/j.knosys.2026.116353_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2024.112031","article-title":"GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning","volume":"212","author":"Lu","year":"2024","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.knosys.2026.116353_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.jss.2024.112234","article-title":"Dlap: A deep learning augmented large language model prompting framework for software vulnerability detection","volume":"219","author":"Yang","year":"2025","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.knosys.2026.116353_b38","article-title":"Vul-rag: Enhancing llm-based vulnerability detection via knowledge-level rag","author":"Du","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"10.1016\/j.knosys.2026.116353_b39","doi-asserted-by":"crossref","first-page":"212815","DOI":"10.1109\/ACCESS.2025.3638251","article-title":"Proverag: Provenance-driven vulnerability analysis with automated retrieval-augmented llms","volume":"13","author":"Fayyazi","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2026.116353_b40","series-title":"Findings of the Association for Computational Linguistics: ACL 2024","first-page":"10507","article-title":"Generalization-enhanced code vulnerability detection via multi-task instruction fine-tuning","author":"Du","year":"2024"},{"key":"10.1016\/j.knosys.2026.116353_b41","series-title":"Security vulnerability detection with multitask self-instructed fine-tuning of large language models","author":"Yang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116353_b42","series-title":"Neural machine translation of rare words with subword units","author":"Sennrich","year":"2015"},{"key":"10.1016\/j.knosys.2026.116353_b43","doi-asserted-by":"crossref","unstructured":"Y. Wan, W. Zhao, H. Zhang, T. Zhang, M. Sun, What Do They Capture? A Structural Analysis of Pre-Trained Language Models for Source Code, in: Proceedings of the 44th International Conference on Software Engineering, ICSE, 2022, pp. 2377\u20132388.","DOI":"10.1145\/3510003.3510050"},{"key":"10.1016\/j.knosys.2026.116353_b44","series-title":"Esimcse: Enhanced sample building method for contrastive learning of unsupervised sentence embedding","author":"Wu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b45","series-title":"Simcse: Simple contrastive learning of sentence embeddings","author":"Gao","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b46","first-page":"27865","article-title":"Self-supervised bug detection and repair","volume":"34","author":"Allamanis","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"10.1016\/j.knosys.2026.116353_b47","series-title":"Contrastive learning with hard negative samples","author":"Robinson","year":"2020"},{"key":"10.1016\/j.knosys.2026.116353_b48","doi-asserted-by":"crossref","unstructured":"K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum Contrast for Unsupervised Visual Representation Learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.knosys.2026.116353_b49","doi-asserted-by":"crossref","unstructured":"Z. Wu, Y. Xiong, S.X. Yu, D. Lin, Unsupervised Feature Learning via Non-Parametric Instance Discrimination, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 3733\u20133742.","DOI":"10.1109\/CVPR.2018.00393"},{"key":"10.1016\/j.knosys.2026.116353_b50","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"10.1016\/j.knosys.2026.116353_b51","series-title":"Tree-sitter","year":"2024"},{"issue":"1","key":"10.1016\/j.knosys.2026.116353_b52","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1017\/S1351324916000334","article-title":"Word2Vec","volume":"23","author":"Church","year":"2017","journal-title":"Nat. Lang. Eng. (NLE)"},{"key":"10.1016\/j.knosys.2026.116353_b53","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.knosys.2026.116353_b54","series-title":"2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"1735","article-title":"Dimensionality reduction by learning an invariant map","volume":"vol. 2","author":"Hadsell","year":"2006"},{"key":"10.1016\/j.knosys.2026.116353_b55","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"An end-to-end deep learning architecture for graph classification","volume":"vol. 32","author":"Zhang","year":"2018"},{"issue":"3","key":"10.1016\/j.knosys.2026.116353_b56","doi-asserted-by":"crossref","first-page":"679","DOI":"10.3390\/pr11030679","article-title":"Classification of tumor in brain MR images using deep convolutional neural network and global average pooling","volume":"11","author":"Malla","year":"2023","journal-title":"Processes"},{"key":"10.1016\/j.knosys.2026.116353_b57","series-title":"International Conference on Machine Learning","first-page":"23803","article-title":"Cross-entropy loss functions: Theoretical analysis and applications","author":"Mao","year":"2023"},{"key":"10.1016\/j.knosys.2026.116353_b58","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"10665","article-title":"Adahessian: An adaptive second order optimizer for machine learning","volume":"vol. 35","author":"Yao","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b59","series-title":"Starcoder 2 and the stack v2: The next generation","author":"Lozhkov","year":"2024"},{"key":"10.1016\/j.knosys.2026.116353_b60","series-title":"Codexglue: A machine learning benchmark dataset for code understanding and generation","author":"Lu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116353_b61","doi-asserted-by":"crossref","unstructured":"J. Fan, Y. Li, S. Wang, X. Xiao, L. Zhang, A C\/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries, in: Proceedings of the 17th International Conference on Mining Software Repositories, MSR, 2020, pp. 508\u2013512.","DOI":"10.1145\/3379597.3387501"},{"key":"10.1016\/j.knosys.2026.116353_b62","doi-asserted-by":"crossref","unstructured":"V.A. Nguyen, D.Q. Nguyen, V. Nguyen, T.T. Pham, T.N. Nguyen, Regvd: Revisiting Graph Neural Networks for Vulnerability Detection, in: Proceedings of the ACM\/IEEE 44th International Conference on Software Engineering: Companion Proceedings (ICSE Companion), 2022, pp. 178\u2013182.","DOI":"10.1145\/3510454.3516865"},{"key":"10.1016\/j.knosys.2026.116353_b63","doi-asserted-by":"crossref","unstructured":"C. Ni, X. Yin, K. Yang, L. Tan, T. Chen, J. Liu, Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation, in: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE), 2023, pp. 1611\u20131622.","DOI":"10.1145\/3611643.3616358"},{"key":"10.1016\/j.knosys.2026.116353_b64","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.103994","article-title":"SCL-CVD: Supervised contrastive learning for code vulnerability detection via GraphCodeBERT","volume":"145","author":"Wang","year":"2024","journal-title":"Comput. Secur."},{"key":"10.1016\/j.knosys.2026.116353_b65","series-title":"Qwen2.5-coder technical report","author":"Hui","year":"2024"},{"key":"10.1016\/j.knosys.2026.116353_b66","series-title":"Code llama: Open foundation models for code","author":"Roziere","year":"2023"},{"key":"10.1016\/j.knosys.2026.116353_b67","series-title":"Deepseek-coder-V2: Breaking the barrier of closed-source models in code intelligence","author":"Zhu","year":"2024"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010798?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010798?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T19:35:18Z","timestamp":1783107318000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126010798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":67,"alternative-id":["S0950705126010798"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116353","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Vul-CGBT: Enhancing code vulnerability detection via contrastive semantic learning and graph embedding","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116353","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116353"}}