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

Nerurkar, Pranav; Patel, Dhiren; Busnel, Yann; Ludinard, Romaric; Kumari, Saru; Khan, Muhammad Khurram

doi:10.1016/j.jnca.2020.102940

Cited by 51 publications

(29 citation statements)

References 25 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It implies that the Bitcoin network is a scale-free network, in which only a few nodes have a large number of connections [21] while the majority of nodes are of low degrees and have fewer connections. This is consistent with the findings obtained by the research [7], [8] using complete real network data.…”

Section: A Degree Distributionsupporting

confidence: 93%

“…Refs. [6] and [7] initially explore the Bitcoin transaction network with a preliminary graph analysis while [8] provides the longitudinal network-based analysis of the Bitcoin systems. The study [9] analyzes the structure of the transaction network by measuring network characteristics, including the degree distribution, degree correlations, and clustering.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, the research on the Bitcoin transaction network is still quite limited. Existing Bitcoin network research primarily focuses on large-scale and long-term data analysis of the entire network [7] [8], or transaction pattern detection [10], but lacks both in-depth exploration on network characteristics and computationally-efficient algorithms of networks analysis. For example, the Bitcoin transaction network's characteristics such as assortativity, connection tendency, and centrality need to be further investigated by a computationallyefficient network analysis method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Complex Network Analysis of the Bitcoin Transaction Network

Tao,

Dai,

et al. 2022

Preprint

View full text Add to dashboard Cite

In this brief, we conduct a complex-network analysis of the Bitcoin transaction network. In particular, we design a new sampling method, namely random walk with flying-back (RWFB), to conduct effective data sampling. We then conduct a comprehensive analysis of the Bitcoin network in terms of the degree distribution, clustering coefficient, the shortest-path length, connected component, centrality, assortativity, and the rich-club coefficient. We obtain several important observations including the small-world phenomenon, multi-center status, preferential attachment, and non-rich-club effect of the current network. This work brings up an in-depth understanding of the current Bitcoin blockchain network and offers implications for future directions in malicious activity and fraud detection in cryptocurrency blockchain networks.

show abstract

Section: A Degree Distributionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complex Network Analysis of the Bitcoin Transaction Network

Tao,

Dai,

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, most studies in this area still overlooked detailed analyses of the network community structures. While extensive studies have been performed on blockchain networks such as Bitcoin ( Nerurkar et al, 2021 ) due to its long establishment, network analyses on Ethereum are quite limited ( Li et al, 2020 ). Such analyses could play a crucial role in wealth distribution, the network’s relational structure, and the link predictability from heterogeneous network data.…”

Section: Introductionmentioning

confidence: 99%

Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction

Said

Janjua

Hassan

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Ethereum, the second-largest cryptocurrency after Bitcoin, has attracted wide attention in the last few years and accumulated significant transaction records. However, the underlying Ethereum network structure is still relatively unexplored. Also, very few attempts have been made to perform link predictability on the Ethereum transactions network. This paper presents a Detailed Analysis of the Ethereum Network on Transaction Behavior, Community Structure, and Link Prediction (DANET) framework to investigate various valuable aspects of the Ethereum network. Specifically, we explore the change in wealth distribution and accumulation on Ethereum Featured Transactional Network (EFTN) and further study its community structure. We further hunt for a suitable link predictability model on EFTN by employing state-of-the-art Variational Graph Auto-Encoders. The link prediction experimental results demonstrate the superiority of outstanding prediction accuracy on Ethereum networks. Moreover, the statistic usages of the Ethereum network are visualized and summarized through the experiments allowing us to formulate conjectures on the current use of this technology and future development.

show abstract

“…Already we know that Artificial Intelligence (AI) and deep learning are popular methodologies for application in domains where data is available in text, audio, video, or speech formats [15,16,17,18,19,20]. AI and profound learning have brought perspective changes in medical care examination fields, for example, X-beam finding, EEG/ECG (Electroencephalography/electrocardiography) analysis, and so on Nonetheless, restricted accessibility of databases because of trouble in information assortment and marking have hampered the accomplishment of the cutting edge brings about hack conclusion [1,21].…”

mentioning

confidence: 99%

Deep Learning and Internet of Things Based Lung Ailment Recognition Through Coughing Spectrograms

et al. 2021

View full text Add to dashboard Cite

Coughing analysis stays a region that has gotten meager consideration from AI scientists. This can be credited to a few factors, for example, wasteful auxiliary frameworks, high costs in getting databases, or trouble in building classifiers. The current paper classifies and audits the advancement on coughing sound investigation, AI models, and the information assortment strategies through IoT (Internet of Things) for the grouping of pulmonary sicknesses. Moreover, it proposes a Multi-layered Convolutional Neural Network (Deep Convolutional Neural Network-DCNN) for the arrangement of eight pneumonic infections. The DCNN utilizes otherworldly highlights, cepstral coefficients, chroma highlights, and spectrograms from coughing sound for preparing. To test the viability of the model, a similar report with four standard models was directed on a database of 112 patients gathered from a pediatric office in India through a cloud server and wearable electronic sensors. Results demonstrated that the proposed model accomplished an accuracy of 0.4 on the test segment, which was practically equivalent to recent models proposed in the writing overviewed. INDEX TERMSWearable electronic sensors, Internet of Things, Cloud Computing, Coughing spectrograms, Spectral and Chroma features, Cepstral Coefficients. I. BACKGROUND VOLUME 4

show abstract

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

Cited by 51 publications

References 25 publications

Complex Network Analysis of the Bitcoin Transaction Network

Complex Network Analysis of the Bitcoin Transaction Network

Detailed analysis of Ethereum network on transaction behavior, community structure and link prediction

Deep Learning and Internet of Things Based Lung Ailment Recognition Through Coughing Spectrograms

Contact Info

Product

Resources

About