Graph-Neural-Network-Based Transaction Prediction Method for Public Blockchain in Heterogeneous Information Networks
Public blockchain has outstanding performance in transaction privacy protection because of its anonymity. The data openness brings feasibility to transaction behavior analysis. At present, the transaction data of the public chain are huge, including complex trading objects and relationships. It is difficult to extract attributes and predict transaction behavior by traditional methods. To solve the problems, we extract the transaction features to construct the Ethereum transaction heterogeneous information network (HIN), and propose graph-neural-network-based transaction prediction method for public blockchain in HINs, which can divide the network into subgraphs according to connectivity and make the prediction results of transaction behavior more accurate. Experiments show that the execution time consumption of the proposed transaction subgraph division method is reduced by 70.61% on average compared with the search method. The accuracy of the proposed behavior prediction method also improve compared with the traditional random walk method, with an average accuracy of 83.82%.
Fri 26 JulDisplayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change
15:30 - 16:45 | Session 3 - Transaction AnanlysisResearch Track at The ballroom B Chair(s): Cheqing Jin East China Normal University | ||
15:30 15mPaper | Transaction spatio-temporal distribution for blockchain performance profiling Research Track Jianhuan Mao Beihang University, Mengxiao Zhu North China University of Technology, Yi Sun Chinese Academy of Sciences, Lei Li Zhongguancun Laboratory, Haogang Zhu Beihang University | ||
15:45 15mPaper | Research on the Application of Large Language Model-Enhanced Graph Neural Networks in Ethereum Phishing Fraud Detection Research Track Rong Xu Inner Mongolia University, Xiaowei Ding Nanjing University, Jun Zhang Inner Mongolia University, He Li Inner Mongolia University | ||
16:00 15mPaper | Scam Token Classification for Decentralized Exchange Using Transaction Data Research Track Vladislav Amelin GBC.AI Pty Ltd, Australia, Ahmad Salehi Shahraki La Trobe University, Australia, Suparat Srifa SparkBeyond Ltd., Thailand, Tharuka Rupasinghe RMIT University, Australia, Robert Vasilyev GBC.AI Pty Ltd, Australia, Yury Yanovich Skolkovo Institute of Science and Technology; Faculty of Computer Science, HSE University | ||
16:15 15mPaper | Cardano Shared Send Transactions Untangling in Numbers Research Track Mostafa Chegenizadeh University of Zurich, Nickolay Larionov Moscow Institute of Physics and Technology, Sina Rafati Niya University of Zurich, Yury Yanovich Skolkovo Institute of Science and Technology; Faculty of Computer Science, HSE University, Claudio J. Tessone University of Zurich | ||
16:30 15mPaper | Graph-Neural-Network-Based Transaction Prediction Method for Public Blockchain in Heterogeneous Information Networks Research Track Zening Zhao Tianjin University of Technology, Jinsong Wang Tianjin University of Technology, Jiajia Wei Tianjin University of Technology | ||