高技术通讯2026，Vol.36Issue(3)：244-255,12.DOI:10.3772/j.issn.1002-0470.2026.03.003
基于自适应增量计算模型的流式图分析优化
Optimization of stream graph analysis based on adaptive incremental computation model
摘要
Abstract
Stream graphs are widely applied in fields such as computer networks,map planning,and social networks.Real-time computation is crucial for processing stream graphs,but traditional methods require recomputing the en-tire dataset,leading to low efficiency and high resource consumption.Incremental computation reduces the compu-tational load by correcting historical query results;however,as the number of iterations increases,its efficiency may decline.This paper proposes an innovative optimization method for graph computation,employing machine learning and mathematical modeling to evaluate the performance of different computation models.It introduces a model switching strategy based on the number of iterations,effectively combining the advantages of traditional and incremental computation to significantly enhance computational efficiency.Additionally,to address memory over-head issues,this study proposes a method to reduce the number of iterations in incremental computation,thereby lowering memory usage.After optimization,the system achieved performance improvements of 1.39 times and 1.14 times compared to traditional and incremental computation models,respectively.关键词
流式图/增量计算/传统计算/流式图处理Key words
stream graph/incremental computing/traditional computing/streaming graph processing引用本文复制引用
梁彦,王春祥,曹华伟,范东睿..基于自适应增量计算模型的流式图分析优化[J].高技术通讯,2026,36(3):244-255,12.基金项目
国家重点研发计划(2023YFB4502305)和北京市自然科学基金(4232036)资助项目. （2023YFB4502305）
