Topology Measurement and Analysis on Ethereum P2P Network Ethereum, one of the most popular cryptocurrencies, has attracted increasing attention of people in various fields. As the backbone of Ethereum, its peertopeer network has an effect on almost every aspect of the ecosystem. Consequently, itxe2x80x99s necessary to understand the topological properties of Ethereum P2P network. In this paper, we conducted a measurement of Ethereum P2P network. Our result shows that the graphs of Ethereum network have a small average shortest path length and a large clustering coefficient, and the degree distribution of nodes does not follow a pure powerlaw distribution. These indicate that Ethereum network is very close to a small world network. Though there are a large number of stale nodes and useless nodes, Ethereum is still resilient to both random failures and targeted attacks. Whatxe2x80x99s more, we find that there are around one hundred abnormal nodes in the network. The IP addresses of nodes included in the neighbors messages they reply are replaced with their own IP addresses. Those nodes might have a bad influence on network routing.

ChainNet: Learning on Blockchain Graphs with Topological Features With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction. Unlike other financial networks, such as stock and currency trading, blockchain based cryptocurrencies have the entire transaction graph accessible to the public (i.e., all transactions can be downloaded and analyzed). A natural question is then to ask whether dynamics of the transaction graph impacts price of the underlying cryptocurrency. We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price. In contrast, topological features computed from the blockchain graph using the tools of persistent homology, are found to exhibit higher utility for predicting Bitcoin price dynamics.