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

Abstract

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.

Disorder unleashes panic in bitcoin dynamics

“…Before moving to the details of the proposed model and related results, we remark that the Blockchain and cryptocurrencies constitute a modern and expanding research area. Just to cite a few, previous investigations studied the Bitcoin price dynamics [8][9][10][11], the crypto network of transactions [12][13][14][15][16][17][18][19], the predictive signals [20], using social data [21][22][23][24] and machine learning-based approaches [25,26], and the interplay between the network of Bitcoin transactions and the crypto market [27].…”

Illegal Community Detection in Bitcoin Transaction Networks

“…It can also be modelled as a network of users where users are vertices, and a link between users indicates the flow of BTC from one user to another. Vallarano et al [4] used this modelling to investigate the relationship between users' behaviour and cryptocurrency pricing in exchange markets by focusing on Bitcoin, while De Collibus et al [5,6] focused on understanding the growth of the Ethereum network. Other works focused on the forensics, such as entity identification [7,8] and using machine learning to detect illegal transactions [9][10][11].…”

The Weighted Bitcoin Lightning Network

“…the capacity of the channel it depicts.. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) transaction networks such as Bitcoin, Bitcoin Cash, Dash, Dogecoin, Ethereum, Feathercoin, Litecoin, Monacoin and Z-Cash [9][10][11][12] -may have undesirable consequences such as causing a considerable fraction of payments to be easily de-anonymizable [5] and making it prone to channel exhaustion or attacks aimed at isolating nodes (thus, compromising their reachability, the payment success ratio, etc.) [13,14]; a similar conclusion is reached in [15], where the authors analyze the robustness of the BLN against three different types of attacks (locking channels, disconnecting pairs of nodes and isolating hubs) and find it to be disruptable at a relatively low cost; still, Conoscenti et al [16] have suggested the BLN to be resilient against the removal of nodes that do not have a significant influence on the probability of success of a payment.…”

“…∼ ln 𝑁 (12) i.e. if the average path length grows logarithmically with the number of nodes and if the average clustering coefficient BCC 𝑖 = ∑ 𝑁 𝑖=1 BCC 𝑖 ∕𝑁 is larger than the one predicted by an Undirected Random Graph Model (URGM) tuned to reproduce the empirical density of links.…”