Automatic blockchain whitepapers analysis via heterogeneous graph neural network

【Author】 Liu, Lin; Tsai, Wei-Tek; Bhuiyan, Md Zakirul Alam; Yang, Dong

【Source】JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

【影响因子】4.542

【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%similar to 33.34% in terms of F1-score for classification task, and 4.94%similar to 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. (C) 2020 Elsevier Inc. All rights reserved.

【Keywords】Blockchain; Heterogeneous graph neural network; Classification; Clustering; Heterogeneous information networks

【发表时间】2020 NOV

【收录时间】2022-01-02

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