面向复杂网络图的环路挖掘算法研究进展OA
Research Progress on Cycle Mining Algorithm for Complex Network Graphs
环路作为一种特殊的模体,在揭示图的结构特征方面发挥着不可或缺的作用,能够帮助人们深入理解图的结构特点,得出更有价值的结论和科学预测.因此,环路挖掘一直是图研究领域中的热点问题之一.文章紧扣环路挖掘问题的研究脉络,从静态图和动态图两个维度,对图上的环路挖掘算法进行了全面细致的梳理,同时选取其中部分经典算法以更直观的方式展开进一步对比分析,并列出了相关研究所涉及的数据集与其对应的下载链接,便于读者能够更便捷地获取所需资源,展开进一步的探索与研究.
As a special motif,cycle plays an indispensable role in revealing the structural characteristics of graphs,which can help people deeply understand the structural characteristics of graphs and draw more valuable conclusions and scientific prediction.Therefore,cycle mining has always been one of the hot issues in the field of graph research.This paper closely follows the research context of cycle mining problems,comprehensively and meticulously sorts out the cycle mining algorithm on the graph from the two dimensions of static graph and dynamic graph.And it selects some of the classic algorithms to carry out further comparative analysis in a more intuitive way,and lists the datasets involved in related studies and their corresponding download links,so that readers can obtain the required resources more conveniently and carry out further exploration and research.
刘笑婷
河南财经政法大学 计算机与信息工程学院,河南 郑州 450046
计算机与自动化
图数据挖掘有向图时序图环路
graph data miningdirected graphtemporal graphcycle
《现代信息科技》 2024 (018)
33-38 / 6
河南省高等学校重点科研项目(24A520001)
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