Automatic blockchain whitepapers analysis via heterogeneous graph neural network

Author:Lin Liu , Wei-Tek Tsai, Md Zakirul Alam Bhuiyan, Dong Yang  

Affiliations: State Key Laboratory of Software Environment, Beihang University, Beijing, China

Digital Society & Blockchain Laboratory, Beihang University, Beijing, China

School of Computer Science and Engineering, Beihang University, Beijing, China

Arizona State University, Tempe, AZ 85287, USA

Beijing Tiande Technologies, Beijing, China

Andrew International Sandbox Institute, Qingdao, China

IOB Laboratory, National BigData Comprehensive Experimental Area, Guizhou, China

Department of Computer and Information Sciences, Fordham University, NY, USA

Abstract:The blockchain whitepaper contains detailed technical and business information, so its analysis is important for blockchain text mining. Previous works focus on analyze homogeneous objects and relations. The main problem, however, is these works do not take into account the heterogeneity of information. This paper presents a new methodology for whitepapers analysis by designing heterogeneous graph neural network, named S-HGNN. In detail, this paper first builds a Heterogeneous Information Network (HIN) using heterogeneous objects and relationships extracted from the whitepaper to obtain similarity measures, then uses Graph Convolutional Network (GCN) and Graph Attention Network (GAT) to integrate both structural information and internal semantic into the whitepaper embedding. Compared with the previous models, this model improves 0.96%33.34% in terms of F1-score for classification task, and 4.94%14.14% in terms of purity for clustering task, and gets stable results on different tasks. The results show the effectiveness and robustness of this model for whitepapers analysis.