A Social Network Approach to Analyzing Token Properties and Abnormal Events in Decentralized Exchanges

TL;DR

This paper uses social network analysis to examine token properties and detect anomalies in decentralized exchanges, revealing scale-free network structures and centrality measures that correlate with market significance.

Contribution

It introduces a novel application of social network analysis to model token relationships in decentralized exchanges and identifies structural patterns useful for anomaly detection.

Findings

Token networks follow a power law distribution, indicating scale-free properties.

Centrality measures correlate with token market significance.

Networks of different exchanges show similar structural patterns.

Abstract

The properties of tokens within the Ethereum blockchain, such as their current prices, trade volumes, and potential future values, have been the subjects of numerous studies. Employing social networks and graphs, as powerful tools for modeling connections within groups or communities would provide valuable guidance for analyzing these properties. This study mainly focuses on creating and examining networks related to two major decentralized exchanges including Uniswap Version 2 (UniswapV2) and SushiSwap. We have discovered that the distribution of nodes' degrees follows a power law that makes them scale-free networks, in addition, the centrality of tokens in exchange graphs provides valuable insights into their price and significance in cryptocurrency markets. These measures of centrality can be used to detect anomalies in cryptocurrency markets and prices. Notably, these networks exhibit remarkably similar structures, hinting at exciting research opportunities for modeling such networks.