Inside Alameda Research: A Multi-Token Network Analysis

We analyze the token transfer network on Ethereum, focusing on accounts associated with Alameda Research, a cryptocurrency trading firm implicated in the misuse of FTX customer funds. Using a multi-token network representation, we examine node centralities and the network backbone to identify critical accounts, tokens, and activity groups. The temporal evolution of Alameda accounts reveals shifts in token accumulation and distribution patterns leading up to its bankruptcy in November 2022. Through network analysis, our work offers insights into the activities and dynamics that shape the DeFi ecosystem.

Temporal and Geographical Analysis of Real Economic Activities in the Bitcoin Blockchain

We study the real economic activity in the Bitcoin blockchain that involves transactions from/to retail users rather than between organizations such as marketplaces, exchanges, or other services. We first introduce a heuristic method to classify Bitcoin players into three main categories: Frequent Receivers (FR), Neighbors of FR, and Others. We show that most real transactions involve Frequent Receivers, representing a small fraction of the total value exchanged according to the blockchain, but a significant fraction of all payments, raising concerns about the centralization of the Bitcoin ecosystem. We also conduct a weekly pattern analysis of activity, providing insights into the geographical location of Bitcoin users and allowing us to quantify the bias of a well-known dataset for actor identification.

Pattern Analysis of Money Flow in the Bitcoin Blockchain

Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows --dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns