Heterogeneous Preferential Attachment in Key Ethereum-Based Cryptoassets

Collibus, Francesco Maria De; Partida, Alberto; Piškorec, Matija; Tessone, Claudio J.

doi:10.3389/fphy.2021.720708

Cited by 8 publications

(15 citation statements)

References 30 publications

(46 reference statements)

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“…An example of this importance is the result that we obtain in Section 3.3 when we study preferential attachment in AMP, DAI, BAT and UNI. Other studies [ 14 , 15 , 40 ] mention as well a heavy tail in the degree distribution for other tokens.…”

Section: Resultsmentioning

confidence: 93%

“…We also explore the evolution of

through time with disjoint and non-cumulative time windows. The size of the transaction networks makes this task computationally demanding, so we only analyse a random 10% sample of the new edges [ 14 ]. We repeat this process multiple times and calculate the average value of

for each window.…”

Section: Resultsmentioning

confidence: 99%

“…This indicates a large structural centralisation where those removed nodes were effectively acting as hubs in the transaction network, involved in the majority of the transactions. In the information security realm, intentional risk managers should protect these nodes the most [ 14 , 15 , 39 , 40 , 41 ]. We also performed additional dismantling for up to 10k nodes for each of the tokens, but this did not show qualitatively different results.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, super-linear probability results in a network where very few nodes tend to connect to all other nodes of the network, effectively acting as super-hubs in the network. This model was used extensively for in-degree in an article on preferential attachment in key Ethereum-based cryptoassets published in 2021 by the authors of this study [ 14 ]. In this case, we extend this preferential attachment model to the out-degree

of an existing node j to model the dynamic process of out-degree consolidation.…”

Section: Methodsmentioning

confidence: 99%

“…ERC-20 tokens on top of the Ethereum blockchain constitute a unique object of study: their transaction networks interact among themselves and with the Ether transaction network. We started to study a limited set of ERC-20 tokens in 2021 [ 14 ]. In this article, we study the most connected addresses in the Ether transaction network and in specific token transaction networks.…”

Section: Introductionmentioning

confidence: 99%

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The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets

Collibus

Piškorec

Partida

et al. 2022

Entropy

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In this paper, we use the methods of networks science to analyse the transaction networks of tokens running on the Ethereum blockchain. We start with a deep dive on four of them: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We study two types of blockchain addresses, smart contracts (SC), which run code, and externally owned accounts (EOA), run by human users, or off-chain code, with the corresponding private keys. We use preferential attachment and network dismantling strategies to evaluate their importance for the network structure. Subsequently, we expand our view to all ERC-20 tokens issued on the Ethereum network. We first study multilayered networks composed of Ether (ETH) and individual tokens using a dismantling approach to assess how the deconstruction starting from one network affects the other. Finally, we analyse the Ether network and Ethereum-based token networks to find similarities between sets of high-degree nodes. For this purpose, we use both the traditional Jaccard Index and a new metric that we introduce, the Ordered Jaccard Index (OJI), which considers the order of the elements in the two sets that are compared. Our findings suggest that smart contracts and exchange-related addresses play a structural role in transaction networks both in DeFi and Ethereum. The presence in the network of nodes associated to addresses of smart contracts and exchanges is positively correlated with the success of the token network measured in terms of network size and market capitalisation. These nodes play a fundamental role in the centralisation of the supposedly decentralised finance (DeFi) ecosystem: without them, their networks would quickly collapse.

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Section: Resultsmentioning

confidence: 93%

“…We also explore the evolution of

for each window.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

of an existing node j to model the dynamic process of out-degree consolidation.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets

Collibus

Piškorec

Partida

et al. 2022

Entropy

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MT$$^2$$AD: multi-layer temporal transaction anomaly detection in ethereum networks with GNN

Han,

Wei,

Wang

et al. 2023

Complex Intell. Syst.

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In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT$$^2$$ 2 AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT$$^2$$ 2 AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform the anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT$$^2$$ 2 AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated.

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