Suspicious trading in nonfungible tokens (NFTs)

Sifat, Imtiaz; Tariq, Syed Ahzam; van Donselaar, Denise

doi:10.1016/j.im.2023.103898

Cited by 5 publications

(1 citation statement)

References 60 publications

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“…High transaction volumes with minimal change in ownership could be flagged for further investigation Hasan et al [92]. Wash trading in the NFT market is a significant concern that requires attention and monitoring to maintain market integrity [93]. Suspicious trading activities, such as wash trading, have been observed in the NFT ecosystem, emphasizing the need for robust detection mechanisms.…”

Section: Nft Manipulation Mappingmentioning

confidence: 99%

Application of Graph Theory for Blockchain Technologies

Jayabalasamy,

Pujol,

Latha Bhaskaran

2024

Mathematics

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Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.

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Section: Nft Manipulation Mappingmentioning

confidence: 99%

Application of Graph Theory for Blockchain Technologies

Jayabalasamy,

Pujol,

Latha Bhaskaran

2024

Mathematics

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The link between abnormal numbers and price movements of financial securities: How does Benford’s law predict stock returns?

Ben Hamida,

de Peretti,

Belkacem

2024

International Review of Financial Analysis

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Correlations versus noise in the NFT market

Wątorek,

Szydło,

Kwapień

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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The non-fungible token (NFT) market emerges as a recent trading innovation leveraging blockchain technology, mirroring the dynamics of the cryptocurrency market. The current study is based on the capitalization changes and transaction volumes across a large number of token collections on the Ethereum platform. In order to deepen the understanding of the market dynamics, the inter-collection dependencies are examined by using the multivariate formalism of detrended correlation coefficient and correlation matrix. It appears that correlation strength is lower here than that observed in previously studied markets. Consequently, the eigenvalue spectra of the correlation matrix more closely follow the Marchenko–Pastur distribution, still, some departures indicating the existence of correlations remain. The comparison of results obtained from the correlation matrix built from the Pearson coefficients and, independently, from the detrended cross-correlation coefficients suggests that the global correlations in the NFT market arise from higher frequency fluctuations. Corresponding minimal spanning trees for capitalization variability exhibit a scale-free character while, for the number of transactions, they are somewhat more decentralized.

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Suspicious trading in nonfungible tokens (NFTs)

Cited by 5 publications

References 60 publications

Application of Graph Theory for Blockchain Technologies

Application of Graph Theory for Blockchain Technologies

The link between abnormal numbers and price movements of financial securities: How does Benford’s law predict stock returns?

Correlations versus noise in the NFT market

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