计算机科学 ›› 2026, Vol. 53 ›› Issue (4): 208-214.doi: 10.11896/jsjkx.250600216
秦海棋, 米据生
QIN Haiqi, MI Jusheng
摘要： 数据分析中,从网络中进行概念认知学习是网络背景下的机器学习或人工智能领域的重要问题。将认知算子应用于复杂网络,提出了网络认知概念,通过邻接矩阵和节点度来量化网络特征。进而讨论了动态权重网络的概念,分析了节点连接强度随时间变化的情况,并提出了动态权重网络认知概念的定义。此外,还给出了面向对象、属性和混合更新的增量计算机制,以应对网络节点的动态扩展、边属性的演化以及复合更新等场景。在动态权重网络中,提出了一种局部更新的方法,即通过滑动窗口机制和触发式更新两种方法高效处理边权值的变化,以减轻计算负担并提高效率。总体而言,通过引入认知算子和动态权重网络的概念,提供了一种分析和更新复杂网络中节点影响力的新方法。
中图分类号:
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