Graph structure and statistical properties of Ethereum transaction relationships

Guo, Dongchao; Dong, Jiaqing; Wang, Kai

doi:10.1016/j.ins.2019.04.013

Cited by 60 publications

(43 citation statements)

References 22 publications

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“…Type I network, or the original cryptocurrency transfer network, uses EOAs and CoAs as nodes in the network and the flow of non-zero original cryptocurrency as directed edges [68], [76]. The edges are directed and weighted by the amount sent in the transactions.…”

Section: Account-based Transaction Networkmentioning

confidence: 99%

“…The degree distribution of the Bitcoin network (Types I and III) converged to a powerlaw distribution gradually over time, resulting in a scale-free network around 2010 [69], [75], [85]. The Type I [68], [76] and Type III [88] Ethereum networks also exhibit power-law degree distributions with the power-law exponent γ ≈ 2.…”

Section: A Centralized Networkmentioning

confidence: 99%

“…This connection pattern is also referred to as disassortative mixing, e.g., nodes tending to connect to those with different structural properties. Most early-stage cryptocurrency transaction networks, such as the Type I Bitcoin network from 2009 to 2013 [83], the Type I Litecoin network from 2009 to 2010 [69], and the Type I Ethereum network from 2015 to 2017 [76] all showed disassortative mixing. In the cryptocurrency economy, disassortative mixing reveals that most transactions happen between end-users and popular services such as cryptocurrency exchanges, wallets, gaming, and gambling services, yet less frequently among the end-users themselves.…”

Section: A Centralized Networkmentioning

confidence: 99%

See 2 more Smart Citations

Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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Cryptocurrencies gain trust in users by publicly disclosing the full creation and transaction history. In return, the transaction history faithfully records the whole spectrum of cryptocurrency user behaviors. This article analyzes and summarizes the existing research on knowledge discovery in the cryptocurrency transactions using data mining techniques. Specifically, we classify the existing research into three aspects, i.e., transaction tracings and blockchain address linking, the analyses of collective user behaviors, and the study of individual user behaviors. For each aspect, we present the problems, summarize the methodologies, and discuss major findings in the literature. Furthermore, an enumeration of transaction data parsing and visualization tools and services is also provided. Finally, we outline several gaps and trends for future investigation in this research area.

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Section: Account-based Transaction Networkmentioning

confidence: 99%

Section: A Centralized Networkmentioning

confidence: 99%

Section: A Centralized Networkmentioning

confidence: 99%

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Knowledge Discovery in Cryptocurrency Transactions: A Survey

et al. 2021

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“…In another study by Guo et al, the Ethereum transaction graph is investigated. However, this study only deals with a small part of the blockchain at two specific time spots and is not showing the dynamics of the graph over time (Guo et al 2019). Liang et al in (2018), have compared the transaction graphs of Bitcoin, Ethereum and Namecoin.…”

Section: Ethereummentioning

confidence: 99%

Quantitative analysis of cryptocurrencies transaction graph

Motamed

Bahrak

2019

Appl Netw Sci

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Cryptocurrencies as a new way of transferring assets and securing financial transactions have gained popularity in recent years. Transactions in cryptocurrencies are publicly available, hence, statistical studies on different aspects of these currencies are possible. However, previous statistical analysis on cryptocurrencies transactions have been very limited and mostly devoted to Bitcoin, with no comprehensive comparison between these currencies. In this study, we intend to compare the transaction graph of Bitcoin, Ethereum, Litecoin, Dash, and Z-Cash, with respect to the dynamics of their transaction graphs over time, and discuss their properties. In particular, we observed that the growth rate of the nodes and edges of the transaction graphs, and the density of these graphs, are closely related to the price of these currencies. We also found that the transaction graph of these currencies is non-assortative, i.e. addresses do not tend for transact with a particular type of addresses of higher or lower degree, and the degree sequence of their transaction graph follows the power law distribution.

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“…Moreover, it uses a Delegated Proof of Stake (DPoS) consensus protocol, which makes possible the creation of a new block every 3 seconds. Even if Bitcoin and Ethereum are well-studied [11]- [15], the characteristics of Steem, and how social and economic characteristics can affect each other is still unknown.…”

Section: Introductionmentioning

confidence: 99%

Steem Blockchain: Mining the Inner Structure of the Graph

2020

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Since their introduction, Online Social Networks (OSNs) have transformed the way people interact with each other. Lately, a new trend is rising in the development of OSNs, fueled by an increasing interest of the blockchain technology and the benefits it can bring to the world of OSNs. Blockchain Online Social Media (BOSMs) are Social Media applications that are supported by the blockchain technology. Thanks to a blockchain, BOSMs either try to enforce the privacy of the users or try to redistribute with their users the economic wealth generated by the platform through a rewarding system. There are countless BOSMs available which incorporate a rewarding system. Among them, Steemit can be considered the most wellknown platform exceeding 1 million registered users. Steemit is supported by the blockchain Steem, which is a blockchain that natively supports the development of social applications by the usage of transactions that model social activity. Even if other important blockchains, such as Ethereum has been widely analysed, at the best of our knowledge, no study exists concerning the topology of the transactions graph of Steem. The main goal of this paper is to study the structure of the Steem transaction graph to understand its characteristics and unveil crucial knowledge concerning their users. More in detail, we build the Interactions Graph and, after its study, we evaluate three subgraphs that capture its social and monetary aspects. The degree distributions of the graphs follow a power-law. Additionally, we detect a substantial number of bots that offer paid services on the platform among the most active users. Lastly, the investigation of the four analysed graphs through a bow-tie structure, suggesting that half of the users have a passive social behaviour and that 80% of the users tend to accrue economic value.

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Graph structure and statistical properties of Ethereum transaction relationships

Cited by 60 publications

References 22 publications

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Knowledge Discovery in Cryptocurrency Transactions: A Survey

Quantitative analysis of cryptocurrencies transaction graph

Steem Blockchain: Mining the Inner Structure of the Graph

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