Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach

Abstract

As the largest public blockchain-based platform supporting smart contracts, Ethereum has accumulated a large number of user transaction records since its debut in 2014. Analysis of Ethereum transaction records, however, is still relatively unexplored till now. Modeling the transaction records as a static simple graph, existing methods are unable to accurately characterize the temporal and multiplex features of the edges. In this brief, we first model the Ethereum transaction records as a complex network by incorporating time and amount features of the transactions, and then design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network. Experiments of temporal link prediction on real Ethereum data demonstrate that temporal information and multiplicity characteristic of edges are indispensable for accurate modeling and understanding of Ethereum transaction networks.

5 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1905.08038

References in corpus (3)

• DeepWalk: Online Learning of Social Representations
• Maps of random walks on complex networks reveal community structure
• Link Prediction Based on Local Random Walk

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