《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.
Keywords:Blockchain