Netwulf: Interactive visualization of networks in Python

Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

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“…For visualization purposes, we selected the ten top collections in the Art category on February 2021. Visualization is done using Netwulf 31 . …”

Section: Resultsmentioning

confidence: 99%

Mapping the NFT revolution: market trends, trade networks, and visual features

Nadini

Alessandretti

Giacinto

et al. 2021

Sci Rep

404

141

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“…The current flow betweenness centrality measure is used as a measure of the importance of a feature in the co-enrichment network. Visualization of graphs is performed in Netwulf [46].…”

Section: Methodsmentioning

confidence: 99%

“…The current flow betweenness centrality measure is used as a measure of the importance of a feature in the co-enrichment network. Visualization of graphs is performed in Netwulf[46].The (non-normalized) count data from all cells that passed quality control were combined, and scran deconvolution normalization was performed on the combined data. The data were log-transformed using an alternative pseudocount[50] for use in enrichment scoring.…”

mentioning

confidence: 99%

SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring

Vlot

Ohler

2020

Preprint

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Identification of markers is an essential step in single-cell analytic. Current marker identification strategies typically rely on cluster assignments of cells. Cluster assignment, in particular of development data, is non-trivial, potentially arbitrary and commonly relies on prior knowledge. Yet, cluster uncertainty is not commonly taken into account. In response, we present SEMITONES, a principled method for cluster-free marker identification. We showcase its application on healthy haematopoiesis data as 1) a robust alternative to highly variable gene selection, 2) for marker gene and regulatory region identification, and 3) for the construction of co-enrichment networks that reveal regulators of cell identity.

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“…However, infected traveller may be assigned to a new ride on successive day, resulting with virus propagation beyond the single vehicle. Networks visualized with newtulf 34 . …”

Section: Introductionmentioning

confidence: 99%

Modelling virus spreading in ride-pooling networks

Kucharski

Cats

Sienkiewicz

2021

Sci Rep

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Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

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Netwulf: Interactive visualization of networks in Python

Cited by 19 publications

References 7 publications

Mapping the NFT revolution: market trends, trade networks, and visual features

Mapping the NFT revolution: market trends, trade networks, and visual features

SEMITONES: Single-cEll Marker IdentificaTiON by Enrichment Scoring

Modelling virus spreading in ride-pooling networks

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