Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

Initial Analysis on the Legal Protection of Consumers＇ Rights and Interests in Network Transaction

With the development of network technology and information technology, electronic commerce as a new business model wiht itsnew trading ideas-＋, In changing the traditional business structure,but also gradually changing people’s shopping patterns and consumptionpatterns,Online shopping has become the preferred way of shopping.Changes in consumption patterns will inevitably lead to new problems inthe process of consumption,and the development speed of the problem in sync with the development speed of network economy,as far as therenewal speed of laws and regulations is concerned,this asymmetric development is bound to bring great inconvenience to the consumer rights innetwork transactions.In this paper, from the angle of legal protection,the protection of consumer rights and interests in network transactions areexpounded,analysis of the current situation and reasons of the consumers’ rights and interests are infringed upon in the network transaction,learnfrom the experience of foreign consumer rights and interests protection,In order to put forward effective suggestions on the system constructionof the protection of consumer rights and interests.

Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China

With the gradual application of central bank digital currency(CBDC)in China,it brings new payment methods,but also potentially derives new money laundering paths.Two typical application scenarios of CBDC are considered,namely the anonymous transaction scenario and real-name transaction scenario.First,starting from the interaction network of transactional groups,the degree distribution,density,and modularity of normal and money laundering transactions in two transaction scenarios are compared and analyzed,so as to clarify the characteristics and paths of money laundering transactions.Then,according to the two typical application scenarios,different transaction datasets are selected,and different models are used to train the models on the recognition of money laundering behaviors in the two datasets.Among them,in the anonymous transaction scenario,the graph convolutional neural network is used to identify the spatial structure,the recurrent neural network is fused to obtain the dynamic pattern,and the model ChebNet-GRU is constructed.The constructed ChebNet-GRU model has the best effect in the recognition of money laundering behavior,with a precision of 94.3%,a recall of 59.5%,an F1 score of 72.9%,and a microaverage F1 score of 97.1%.While in the real-name transaction scenario,the traditional machine learning method is far better than the deep learning method,and the micro-average F1 score of the random forest and XGBoost models both reach 99.9%,which can effectively identify money laundering in currency transactions.