We explore the adoption of graph representation learning (GRL) algorithms to
investigate similarities across services offered by Decentralized Finance
(DeFi) protocols. Following existing literature, we use Ethereum transaction
data to identify the DeFi building blocks. These are sets of protocol-specific
smart contracts that are utilized in combination within single transactions and
encapsulate the logic to conduct specific financial services such as swapping
or lending cryptoassets. We propose a method to categorize these blocks into
clusters based on their smart contract attributes and the graph structure of
their smart contract calls. We employ GRL to create embedding vectors from
building blocks and agglomerative models for clustering them. To evaluate
whether they are effectively grouped in clusters of similar functionalities, we
associate them with eight financial functionality categories and use this
information as the target label. We find that in the best-case scenario purity
reaches .888. We use additional information to associate the building blocks
with protocol-specific target labels, obtaining comparable purity (.864) but
higher V-Measure (.571); we discuss plausible explanations for this difference.
In summary, this method helps categorize existing financial products offered by
DeFi protocols, and can effectively automatize the detection of similar DeFi
services, especially within protocols.

我们使用图表示学习算法探索去中心化金融（DeFi）协议提供的服务之间的相似性。我们提出了一种方法，将这些协议特定的智能合约分为簇，并使用聚类模型进行聚类。我们发现该方法有助于对 DeFi 协议提供的现有金融产品进行分类，并能够有效自动化检测类似的 DeFi 服务。