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How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.<\/jats:p>","DOI":"10.3390\/s22093581","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T23:27:25Z","timestamp":1652052445000},"page":"3581","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7431","authenticated-orcid":false,"given":"Lejun","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Yangzhou University, Yangzhou 225127, China"},{"name":"Research and Development Center for E-Learning, Ministry of Education, Beijing 100039, China"},{"name":"Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science Department, City University of Hong Kong, Kowloon Tong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilong","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhennao","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7714-9693","authenticated-orcid":false,"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MIS.2017.49","article-title":"Manufacturing analytics and industrial internet of things","volume":"32","author":"Lade","year":"2017","journal-title":"IEEE Intell. 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