Abstract
Blockchain technology and cryptocurrencies have gained significant attention over the past decade, with Web3 emerging as a decentralized vision for the internet. Cryptocurrencies exhibit high volatility and susceptibility to crashes, necessitating advanced temporal analysis methods that can handle the complexity of blockchain data. Key challenges include determining optimal time scales for analysis, differentiating long-term trends from short-term fluctuations, and identifying shock events in decentralized systems.
This study examines cryptocurrencies traded on the Ethereum blockchain, focusing on the collapse of TerraUSD (UST) and its stabilizing counterpart LUNA. Using complex network analysis and multi-layer temporal graphs, we investigate correlations between currency layers and system evolution across different time scales. Our findings reveal:
- Strong interconnections among stablecoins prior to the crash
- Significant structural transformations post-crash
- Anomalous signals before, during, and after the collapse
- Impact on graph metrics and user migration patterns
The paper introduces temporal, cross-chain graph analysis to cryptocurrency collapse research, demonstrating how graph-based methods can enhance traditional econometric approaches. These insights extend beyond cryptocurrency markets, offering valuable tools for regulatory agencies monitoring financial ecosystems.
Keywords: transaction network, temporal network, cryptocurrency, Web3, multilayer graph
Introduction
The analysis of multi-layer temporal networks presents unique challenges in complex network studies, particularly regarding:
- Optimal time-scale determination
- Trend differentiation (long-term vs. short-term)
- Shock event recognition and characterization
This research focuses on six Ethereum-traded cryptocurrencies, employing multi-layer temporal network analysis to study simultaneous evolution across multiple network layers and time scales.
The May 2022 collapse of TerraUSD (UST) and LUNA serves as a pivotal case study in cryptocurrency volatility. Stablecoins like UST maintain pegged values (typically $1) to facilitate trading rather than speculation. The collapse unfolded rapidly:
- May 7-8, 2022: Initial UST de-pegging
- May 9, 2022 (C): Full collapse ($0.35)
- May 12-13, 2022: LUNA loses 99% value
- May 27, 2022 (T2): Terra 2.0 launch
Our analysis of 26+ million Ethereum transactions surrounding these events provides insights into:
- Pre-crash ecosystem dynamics
- Crash anticipation signals
- Post-crash structural changes
- User behavior shifts
Background: Stablecoins and the Terra Ecosystem
Stablecoins maintain pegged values through:
- Collateralization: Backed by reserve assets
- Algorithmic mechanisms: Automated supply adjustments
Terra's ecosystem featured:
| Currency | Type | Peg Mechanism |
|---|---|---|
| UST | Algorithmic stablecoin | $1 via LUNA swaps |
| LUNA | Volatile cryptocurrency | Stabilized UST |
The collapse timeline reveals coordinated large-scale UST sales potentially intended to:
- Crash UST/LUNA values
- Depress Bitcoin prices
- Profit from short positions
Methodology
Dataset
We analyzed Ethereum transaction data (April-October 2022) covering:
- Top stablecoins: USDT, DAI, USDP, USDC
- Wrapped Terra assets: USTC, WLUNC
Analytical Approach
Multi-layer temporal graph construction:
- Nodes: Ethereum wallets
- Edges: Transactions (directed, weighted)
- Layers: Individual currencies
Temporal windows:
- Pre-crash (30 days before May 1)
- Exclusion zone (15 days centered on May 9)
- Post-crash (30 days after May 17)
Key metrics:
- Cross-layer correlations
- Transaction volume patterns
- Graph structural metrics
- User migration trends
Key Findings
Ecosystem Nature Pre/Post-Crash
Pre-crash:
- High synchronization among stablecoins (r > 0.8)
- 85% users active in single currency
Post-crash:
- Ecosystem bifurcation (USTC/WLUNC vs. others)
- Reduced multi-currency activity among USTC/WLUNC users
Crash Anticipation Signals
Anomalous sales events:
- April 3 (S1): $650M USTC sold
- April 19 (S2): $450M USTC sold
Transaction patterns:
- 5 wallets dominated 80%+ of pre-crash USTC sales
- Coordinated selling behavior evident
Post-Crash Structural Changes
Graph metrics:
- Density: 40% drop in USTC/WLUNC layers
- Clustering: USTC increased 25%
- Components: Temporary fragmentation
User migration:
- 60% USTC traders exited
- Survivors shifted to USDC/USDT
Implications
Regulatory:
- Multi-layer graphs effectively detect coordinated manipulation
- Early warning systems possible for stablecoin pegs
Economic:
- Algorithmic stablecoins show systemic vulnerability
- Cross-currency correlations create contagion risks
Technical:
- Temporal analysis essential for blockchain datasets
- Layer separation crucial for accurate modeling
FAQ
Q: How did the methodology handle Terra-native transactions?
A: The study focused solely on Ethereum-wrapped versions (USTC/WLUNC) due to Terra blockchain data unavailability. While not identical, these show comparable patterns to native transactions.
Q: Could this analysis predict future crashes?
A: While exact prediction remains challenging, the framework identifies:
- Abnormal trading concentrations
- Ecosystem synchronization breakdowns
- Coordinated wallet activity
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Q: What distinguishes this from previous stablecoin research?
A: Prior work typically:
- Combined all currencies into single graphs
- Used price data rather than transaction networks
- Lacked multi-temporal analysis
Q: How significant were the pre-crash signals?
A: The April 3/19 sales represented:
- 15x typical daily USTC volume
- Concentrated in <10 wallets
- Temporally aligned with BTC price pressure
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Conclusion
This research demonstrates how temporal multi-layer graph analysis reveals critical insights into cryptocurrency ecosystem dynamics, particularly during systemic shocks. Key takeaways:
- Stablecoin ecosystems show strong pre-crash synchronization
- Anomalous trading concentrations signal impending instability
- Post-crash user migration follows predictable patterns
- Graph structural changes indicate systemic stress
The methodology provides regulators and researchers with powerful tools for monitoring decentralized financial systems and mitigating collapse risks.