Investigating Similarities Across Decentralized Financial (DeFi)
  Services
Junliang Luo Stefan Kitzler Pietro Saggese
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.