Hodge Decomposition of Bitcoin Money Flow
Hodge Decomposition of Bitcoin Money Flow
复制标题
比特币资金流的霍奇分解
DOI：
10.1007/978-981-15-4498-9_7
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Rubayiat Islam
中科院分区：
文献类型：
--
作者：
Yoshi Fujiwara;Rubayiat Islam
比特币用户之间的资金流动是一个有趣的问题，以了解比特币交易复杂网络的动态，以及交易如何间接与交易所市场的价格相关。我们使用2013年至2018年比特币中的区块链数据（由匈牙利研究小组编制），利用一种简单的算法从地址中部分识别匿名用户，并以用户为节点，以交易为有向链接构建临时变化网络的快照。为了了解用户在整个流程中的位置，特别是上游和下游，我们使用所谓的Hodge分解（或Helmholtz-Hodge-科代拉分解）。此外，我们研究了所谓的“蝴蝶结”结构的二进制网络忽略流，找到用户在上游/下游外围（所谓的IN/OUT）是如何远离核心的强连接组件（SCC）。我们将每个节点的霍奇势与蝴蝶结结构中的这样一个位置进行比较，并与从货币流中测量的每个节点的净需求/供给进行比较，以发现势、拓扑位置和净需求/供给之间的显著相关性。将各环节的流量分解为势流和环流，发现环流对流量的影响是相当显著的。我们将讨论这些发现的可能含义。
How money flows among users of Bitcoin is an interesting question in order to understand the dynamics on the complex network of Bitcoin transactions, and also how the transactions are related to the price in the exchange market indirectly. We employ the data of blockchain in the Bitcoin from 2013 to 2018 (compiled by a Hungary research group), utilize a simple algorithm to partially identify anonymous users from addresses, and construct snapshots of temporarily changing network with the users as nodes and the transactions as directed links. In order to understand how users are located in the entire flow, in particular upstream and downstream, we use the so-called Hodge decomposition (or Helmholtz-Hodge-Kodaira decomposition). In addition, we examine the so-called “bow-tie” structure of the binary network disregarding flow to find how the users in the upstream/downstream peripheries (the so-called IN/OUT) are located away from the core of strongly connected component (SCC). We compare the Hodge potential of each node with such a location in the bow-tie structure, and also with the net demand/supply of each node measured from the money flow, to find a significant correlation among the potential, the topological position, and the net demand/supply. We also decompose the flow of each link into potential flow and circular flow to find that circulation of the flow is quite significant. We shall discuss about possible implication of these findings.
图上的离散霍奇理论：教程
DOI：
--
发表时间：
2013
期刊：
Computing in science & engineering (Print)
影响因子：
--
作者：
James L. Johnson;Tom Goldring
通讯作者：
Tom Goldring
通过每周的网络流量模式揭示比特币区块链中金融机构的身份
影响因子：
0.9
作者：
Rubayiat Islam;Yoshi Fujiwara;Shinya Kawata;Hiwon Yoon
通讯作者：
Hiwon Yoon
日本企业间交易网络的层级循环结构
DOI：
10.2139/ssrn.3173955
发表时间：
2018
期刊：
SSRN
影响因子：
--
作者：
Yuichi Kichikawa;Hiorshi Iyetomi;Takashi Iino;and Hiroyasu Inoue
通讯作者：
and Hiroyasu Inoue