Community Detection in Blockchain Social Networks
arXiv:2101.06406
Abstract
In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network is basically consistent with the ground-truth of betting site community which has been announced to the public. At the meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
References in corpus (11)
- Modularity and community structure in networks
- Fast algorithm for detecting community structure in networks
- A Survey on Security and Privacy Issues of Bitcoin
- Modularity and community detection in bipartite networks
- Applications of Blockchain Technology beyond Cryptocurrency
- Bitcoin Transaction Graph Analysis
- Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods
- Research on Anonymization and De-anonymization in the Bitcoin System
- Social Signals in the Ethereum Trading Network
- Transaction Remote Release (TRR): A New Anonymization Technology for Bitcoin
- SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment