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Consensus and Computing Integration for Processing Transactional Graphs in Consortium Blockchain
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DOI：10.1109/TKDE.2025.3621621.png)
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Abstract
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Recent research and practical scenarios demonstrate that integrating transaction-based graph computing into blockchain has become a critical focus in consortium networks. The on-chain Transactional Graph Processing Applications (TGPAs) have become popular in the blockchain. TGPA leverages blockchain consensus mechanisms to prevent malicious peer attacks while utilizing graph computing to enable powerful analytical capabilities. However, a fundamental challenge exists: blockchain operates on a computation-before-consensus principle, while graph computing requires intermediate result-sharing based on mutual trust. This isolation between the two mechanisms fails to ensure both computational trustworthiness and consensus efficiency. Thus, TGPA necessitates an integrated solution combining consensus and graph computing. Besides, the trusted high-communication environment of graph computing conflicts with the untrusted high-communication environment of blockchain. Communication efficiency and data trustworthiness are existing challenges of the solution. This paper presents a Graph partitioning-based Byzantine Fault Tolerance ($\mathsf {GBFT}$) mechanism for the computing-consensus integration. $\mathsf {GBFT}$ integrates graph computing’s shuffling and merging phases with the consensus phase to achieve parallel computation and synchronized consensus. Additionally, $\mathsf {GBFT}$ incorporates a grouping-partitioning strategy and granular communication methods to enhance both trustworthiness and efficiency. Theoretical analysis proves that $\mathsf {GBFT}$ reduces communication and latency complexity to $O(x)$ ($x$ represents the number of peers). Experimental evaluations demonstrate that $\mathsf {GBFT}$ achieves superior consensus capacity, graph computing capacity, and communication scalability performance. It also supports various graph algorithms across different data scales.
Keywords:
Blockchain
graph computing
consensus
grouping
partitioning
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