Coinbase, a publicly traded U.S.-based cryptocurrency exchange renowned for its development of Base, an Ethereum layer-2 solution, has unveiled a new metric, the h-index, to improve the accuracy of blockchain network adoption tracking. This initiative aims to address distortions arising from airdrop-related activities and Sybil attacks.
In a research report released on Friday, Coinbase highlighted that investments in blockchain infrastructure have resulted in an abundance of blockspace. This surplus has reduced the cost of onchain transactions and spurred the development of decentralized applications. However, this evolution complicates the tracking of ecosystem adoption due to the increased number of applications.
Traditional metrics like total transactions or daily active addresses can be skewed by Sybil attacks and airdrop activities, according to Coinbase. To counter this, the h-index has been introduced, measuring both the depth and breadth of onchain adoption. The h-index quantifies the number of addresses receiving transactions from at least an equivalent number of unique sending addresses.
“For example, an h-index of 100 indicates that 100 different receiving addresses have received transactions from at least 100 unique sending addresses within a specified timeframe,” explained Coinbase.
The application of the h-index revealed that the Ethereum and Base networks exhibited the highest user activity for the week ending June 6, followed by Arbitrum and Polygon. While acknowledging the metric’s limitations, Coinbase believes the h-index offers fresh insights into comparative chain adoption by mitigating the exaggerated effects of Sybil attacks and measuring broader growth.
Nevertheless, Coinbase recognized persistent challenges, including varying blockchain execution environments that affect transaction formats and data interpretation. Furthermore, the influence of exchange or other smart contract wallets could potentially distort these metrics.
Sybil attacks, a common network attack in the crypto industry, involve a single entity creating multiple fake identities or nodes to manipulate network operations. Such activities can skew network metrics by artificially inflating transaction volumes or user activity, thus distorting perceptions of network usage and adoption.