Cross-shard transaction optimization based on community detection in sharding blockchain systems

Blockchain systems have always faced the challenge of performance bottlenecks, and sharding technology is considered a promising mainstream on-chain scalability solution to solve this problem. Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions, it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit. Therefore, this paper aims to reduce the proportion of cross-shard transactions by dividing accounts with frequent transactions into the same shard, thereby improving system throughput. This paper builds a hypergraph based on historical transaction data to represent the diverse transaction relationships between accounts, and formulates the account division problem in the blockchain as a community discovery problem on the hypergraph structure. A time-aware community detection algorithm is proposed to partition accounts by considering the sustainability of transaction relationships between accounts. This also solves the problem of community detection algorithms tending to partition into larger shards. In addition, this paper builds a local Ethereum test network and implements the proposed algorithm on a real transaction dataset. Experimental results show that this algorithm can reduce the proportion of cross-shard transactions from about 95% to about 10%. Furthermore, it shows superior performance in terms of transaction throughput and latency compared with other community detection-based account partitioning algorithms.