A Community Detection-Based Blockchain Sharding Scheme

Zixu Zhang, Xu Wang, Guangsheng Yu, Wei Ni, Ren Ping Liu, Nektarios Georgalas, Andrew Reeves

Abstract: Sharding has been considered a promising approach to improving blockchain scalability. However, multiple shards result in a large number of cross-shard transactions, which require a long confirmation time across shards and thus restrain the scalability of sharded blockchains. In this paper, we convert the blockchain sharding challenge into a graph partitioning problem on undirected and weighted transaction graphs that capture transaction frequency between blockchain addresses. We propose a new sharding scheme using the community detection algorithm, where blockchain nodes in the same community frequently trade with each other. The detected communities are used as shards for node allocation. The proposed community detection-based sharding scheme is validated using public Ethereum transactions over one million blocks. The proposed community detection-based sharding scheme is able to reduce the ratio of cross-shard transactions from 80% to 20%, as compared to baseline random sharding schemes, and retain the ratio of around 20% over the examined one million blocks.