Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network

Lee, Xi Tong; Khan, Arijit; Sengupta, Satarupa; Ong, Yu Hann; Liu, Xuan

doi:10.1145/3366423.3380103

Cited by 60 publications

(29 citation statements)

References 60 publications

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“…The heuristics create an approximation of the original Bitcoin network by merging multiple user identifiers to a single identifier and reducing number of entities in the network. Previous studies on network analysis of cryptocurrencies [12,13,11] as the order of the network, degree distribution, distance, clustering coefficient, and the tendency of exhibiting a "small world" effect [26]. Based on the observations from these measures, the authors have speculated about the online behavior of Ethereum users, the geographic distribution of miner nodes, and the characteristics of transactions.…”

Section: Bitcoin Studiesmentioning

confidence: 99%

“…While S Ferratti et al argued for the advantages of studying the blockchain structure through a complex network perspective, their focus remained on the Ethereum blockchain structure only. X Lee et al studied the Ethereum blockchain at scale and applied network analysis measures to characterize interactions between users in Ethereum [13]. The authors studied the network characteristics (vertex count, edge count, self-loop count, and edge density), local network properties (degree distribution, correlation of out and indegree, node centrality measures) and global network properties (reciprocity, assortativity, connected component distribution, diameter, path length, adhesion, cohesion).…”

Section: Bitcoin Studiesmentioning

confidence: 99%

“…Just like [26], the authors focused on Ethereum blockchain only but have emphasized that a similar line of network analysis could be extended to another web of blockchain networks. The work in the current paper relies on tools and methods given by S Ferratti et al [26] and X Lee et al [13] but targets a longitudinal analysis of Bitcoin network. Table 1 gives the methods and results of network-based studies on blockchain and other real-world systems.…”

Section: Bitcoin Studiesmentioning

confidence: 99%

“…Usually, triangles, transitivity, and clustering coefficient are higher in social networks than non-social networks [13]. These parameters indicate the tendency of entities in the network to form dense communities.…”

Section: Community Structurementioning

confidence: 99%

“…As the scale and complexity of the Bitcoin network increased, research interest too emerged to allow for its better understanding [4,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020)

Nerurkar

Patel²,

Busnel

et al. 2021

Journal of Network and Computer Applications

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Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Unlike fiat currencies, there is no centralized authority or any statutory recognition, backing, or regulation for Bitcoin. All transactions are confirmed for validity by a network of volunteer nodes (miners) and after collective agreement is subsequently recorded into a distributed ledger "Blockchain". Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence.

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Section: Bitcoin Studiesmentioning

confidence: 99%

Section: Bitcoin Studiesmentioning

confidence: 99%

Section: Bitcoin Studiesmentioning

confidence: 99%

Section: Community Structurementioning

confidence: 99%

“…As the scale and complexity of the Bitcoin network increased, research interest too emerged to allow for its better understanding [4,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020)

Nerurkar

Patel²,

Busnel

et al. 2021

Journal of Network and Computer Applications

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Secure smart contracts for cloud‐based manufacturing using Ethereum blockchain

Kumar

Abhishek

Nerurkar

et al. 2020

Trans Emerging Tel Tech

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Past international trade practices have been associated with opaque information flows that have hindered traceability and created hurdles in hassle-free trade. Blockchain and allied technologies have been investigated as a panacea for the problems faced by the Cloud-Based Manufacturing industry. However, previous literature has focused on limited aspects of a typical Cloud-Based Manufacturing chain, such as monitoring assets and securing traceability, which is widely neglecting data integrity and data access. To overcome such drawbacks, the current paper proposes secure smart contracts based on the ERC20 interface in a permissioned blockchain with relevant processes and functions to obtain a holistic framework for securing Cloud-Based Manufacturing operations. The efficacy of the proposed framework was demonstrated on the case study. It was found that critical loopholes in a current supply chain can be overcome using the proposed framework. Additionally, several outlines for future research are outlined.

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Visualization of Ethereum P2P network topology and peer properties

et al. 2021

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Ethereum is arguably the second most popular cryptocurrency-based network after Bitcoin. Both use the distributed ledger technology known as the blockchain, which is considered secure. However, the provided security level is proportional to the number of connected nodes, the number of influential nodes, and the supported amount of hash power. Thus, the knowledge of the network properties and nodes' behavior is helpful to protect the network from possible attacks such as double-spending attacks, DDoS attacks, 51% attacks, and Sybil attacks. This paper proposes a node discovery mechanism, which performs a P2P link discovery on the Ethereum main network. For that, we develop Search-node, a modified version of Ethereum client that searches for all participating nodes in the blockchain network, stores the node information in the Bucket, and then processes the peer discovery method. Based on the collected data, we first visualize the Ethereum network topology and analyze the attributes of the network such as node degree, path length, diameter, and clustering coefficient. We then analyze the node properties and provide analytical results regarding the relationship between nodes, heavily connected nodes, node geo-distribution, security issues, and possible attacks over the influential nodes. As a result, we have identified 68,406 nodes with a total of 642,034 edges. By analyzing the collected data, we have found that the diameter in the Ethereum network is equal to 8. The node degree is over 19, which is two times higher than the default configuration.

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Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network

Cited by 60 publications

References 60 publications

Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020)

Dissecting bitcoin blockchain: Empirical analysis of bitcoin network (2009–2020)

Secure smart contracts for cloud‐based manufacturing using Ethereum blockchain

Visualization of Ethereum P2P network topology and peer properties

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