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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
En 中文
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 <b>T</b>ransactional <b>G</b>raph <b>P</b>rocessing <b>A</b>pplications (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 <b>G</b>raph partitioning-based <b>B</b>yzantine <b>F</b>ault <b>T</b>olerance (<inline-formula><tex-math notation="LaTeX">$\mathsf {GBFT}$</tex-math></inline-formula>) mechanism for the computing-consensus integration. <inline-formula><tex-math notation="LaTeX">$\mathsf {GBFT}$</tex-math></inline-formula> integrates graph computing’s shuffling and merging phases with the consensus phase to achieve parallel computation and synchronized consensus. Additionally, <inline-formula><tex-math notation="LaTeX">$\mathsf {GBFT}$</tex-math></inline-formula> incorporates a grouping-partitioning strategy and granular communication methods to enhance both trustworthiness and efficiency. Theoretical analysis proves that <inline-formula><tex-math notation="LaTeX">$\mathsf {GBFT}$</tex-math></inline-formula> reduces communication and latency complexity to <inline-formula><tex-math notation="LaTeX">$O(x)$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula> represents the number of peers). Experimental evaluations demonstrate that <inline-formula><tex-math notation="LaTeX">$\mathsf {GBFT}$</tex-math></inline-formula> 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
Journal
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