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(C...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.展开更多
This study investigated the characteristics and formation of the online social trust network of Epinions.com, a general consumer review site. An analysis of the static structure of this social trust network revealed a...This study investigated the characteristics and formation of the online social trust network of Epinions.com, a general consumer review site. An analysis of the static structure of this social trust network revealed a high clustering coefficient, short average path length, and power-law degree distribution;it is therefore a small-world and scale-free trust network. The dynamic evolutionary characteristics of the online social network (OSN) were also examined. The results showed that the scale of the network followed a sigmoidal curve;the average degree of the network was nonconstant and changed into a bell-shaped distribution;the density of the network decreased and subsequently stabilized;and user trust diffusion in the network conformed to the Bass model. Finally, the formation of trust within the network was researched at the overall network (macro) and individual user (micro) levels. Compared with their accumulated contribution and reputation, user activeness had a larger effect on trust formation in OSNs, indicating a “diminishing returns” phenomenon. This phenomenon contrasts with the Matthew effect (i.e. , the more reputation a person has, the more likely he or she is to be trusted) in real-world social networks.展开更多
目的研究视觉对人体姿势控制影响及其脑功能网络连接机制。方法以15名健康青年为研究对象,要求受试者分别进行30 s睁眼、闭眼的双腿站立平衡,采集平衡过程中身体压力中心(center of pressure,COP)和脑电。对COP进行样本熵(SampleEn)计算...目的研究视觉对人体姿势控制影响及其脑功能网络连接机制。方法以15名健康青年为研究对象,要求受试者分别进行30 s睁眼、闭眼的双腿站立平衡,采集平衡过程中身体压力中心(center of pressure,COP)和脑电。对COP进行样本熵(SampleEn)计算;对脑电θ、α和β频段,计算相位滞后指数(phase lag index,PLI)构建大脑功能网络,并基于图论计算集聚系数(C)、特征路径长度(L)及小世界网络属性(σ)。结果人体在双腿站立平衡过程中,闭眼COPY样本熵显著高于睁眼(P<0.05)。闭眼α频段PLI平均值显著高于睁眼(P<0.05);闭眼α频段C、σ显著高于睁眼,L显著低于睁眼(P<0.05)。闭眼时α频段额区-中央区-顶区之间的网络连接以及中央区和顶区内连接强度显著高于睁眼(P<0.05)。闭眼时α频段PLI平均值以及C值与COPY样本熵中度呈中度负相关(P<0.05)。睁眼时左前额区、左顶区、左枕区α频段PLI平均值与COPY样本熵呈中度负相关;闭眼时左中央区、右枕区α频段PLI平均值则与COPY样本熵呈中度负相关。结论人体在站立平衡时,当没有视觉信息输入时,身体平衡稳定性下降,同时伴随着脑电α频段的脑网络连接增强以及大脑处理信息的效率需提升。人体在不同的视觉条件下进行姿势控制时,大脑会采用不同的神经策略。展开更多
基金supported in part by the National Natural Science Foundation of China (62272078)the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2021-035A)the Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications (BYJS202009)。
文摘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.
文摘This study investigated the characteristics and formation of the online social trust network of Epinions.com, a general consumer review site. An analysis of the static structure of this social trust network revealed a high clustering coefficient, short average path length, and power-law degree distribution;it is therefore a small-world and scale-free trust network. The dynamic evolutionary characteristics of the online social network (OSN) were also examined. The results showed that the scale of the network followed a sigmoidal curve;the average degree of the network was nonconstant and changed into a bell-shaped distribution;the density of the network decreased and subsequently stabilized;and user trust diffusion in the network conformed to the Bass model. Finally, the formation of trust within the network was researched at the overall network (macro) and individual user (micro) levels. Compared with their accumulated contribution and reputation, user activeness had a larger effect on trust formation in OSNs, indicating a “diminishing returns” phenomenon. This phenomenon contrasts with the Matthew effect (i.e. , the more reputation a person has, the more likely he or she is to be trusted) in real-world social networks.
文摘目的研究视觉对人体姿势控制影响及其脑功能网络连接机制。方法以15名健康青年为研究对象,要求受试者分别进行30 s睁眼、闭眼的双腿站立平衡,采集平衡过程中身体压力中心(center of pressure,COP)和脑电。对COP进行样本熵(SampleEn)计算;对脑电θ、α和β频段,计算相位滞后指数(phase lag index,PLI)构建大脑功能网络,并基于图论计算集聚系数(C)、特征路径长度(L)及小世界网络属性(σ)。结果人体在双腿站立平衡过程中,闭眼COPY样本熵显著高于睁眼(P<0.05)。闭眼α频段PLI平均值显著高于睁眼(P<0.05);闭眼α频段C、σ显著高于睁眼,L显著低于睁眼(P<0.05)。闭眼时α频段额区-中央区-顶区之间的网络连接以及中央区和顶区内连接强度显著高于睁眼(P<0.05)。闭眼时α频段PLI平均值以及C值与COPY样本熵中度呈中度负相关(P<0.05)。睁眼时左前额区、左顶区、左枕区α频段PLI平均值与COPY样本熵呈中度负相关;闭眼时左中央区、右枕区α频段PLI平均值则与COPY样本熵呈中度负相关。结论人体在站立平衡时,当没有视觉信息输入时,身体平衡稳定性下降,同时伴随着脑电α频段的脑网络连接增强以及大脑处理信息的效率需提升。人体在不同的视觉条件下进行姿势控制时,大脑会采用不同的神经策略。
