# T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis

arXiv: https://arxiv.org/abs/1905.08038 · HTML: https://mlsanity.com/paper/1905.08038/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 19 of 19)

- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
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- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
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- [Ethereum Fraud Detection with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2203.12363/) (2022)
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- [Blockchain Phishing Scam Detection via Multi-channel Graph Classification](https://mlsanity.com/paper/2108.08456/) (2021)
- [Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks](https://mlsanity.com/paper/2208.01923/) (2022)
- [From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency](https://mlsanity.com/paper/2309.15133/) (2023)
- [Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks](https://mlsanity.com/paper/2501.12491/) (2025)
- [TEGDetector: A Phishing Detector that Knows Evolving Transaction Behaviors](https://mlsanity.com/paper/2111.15446/) (2021)
- [Detecting DeFi Securities Violations from Token Smart Contract Code](https://mlsanity.com/paper/2112.02731/) (2021)
- [Toward Intention Discovery for Early Malice Detection in Bitcoin](https://mlsanity.com/paper/2209.12001/) (2022)
- [Analysis of Information Propagation in Ethereum Network Using Combined Graph Attention Network and Reinforcement Learning to Optimize Network Efficiency and Scalability](https://mlsanity.com/paper/2311.01406/) (2023)
- [RiskSEA : A Scalable Graph Embedding for Detecting On-chain Fraudulent Activities on the Ethereum Blockchain](https://mlsanity.com/paper/2410.02160/) (2024)
- [Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding](https://mlsanity.com/paper/2503.19926/) (2025)

## Abstract (arXiv)

Recently, graph embedding techniques have been widely used in the analysis of various networks, but most of the existing embedding methods omit the network dynamics and the multiplicity of edges, so it is difficult to accurately describe the detailed characteristics of the transaction networks. Ethereum is a blockchain-based platform supporting smart contracts. The open nature of blockchain makes the transaction data on Ethereum completely public, and also brings unprecedented opportunities for the transaction network analysis. By taking the realistic rules and features of transaction networks into consideration, we first model the Ethereum transaction network as a Temporal Weighted Multidigraph (TWMDG), where each node is a unique Ethereum account and each edge represents a transaction weighted by amount and assigned with timestamp. Then we define the problem of Temporal Weighted Multidigraph Embedding (T-EDGE) by incorporating both temporal and weighted information of the edges, the purpose being to capture more comprehensive properties of dynamic transaction networks. To evaluate the effectiveness of the proposed embedding method, we conduct experiments of node classification on real-world transaction data collected from Ethereum. Experimental results demonstrate that T-EDGE outperforms baseline embedding methods, indicating that time-dependent walks and multiplicity characteristic of edges are informative and essential for time-sensitive transaction networks.