计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 43-48.doi: 10.11896/jsjkx.210800276
李勇, 吴京鹏, 张钟颖, 张强
LI Yong, WU Jing-peng, ZHANG Zhong-ying, ZHANG Qiang
摘要： 链路预测是网络科学的一个重要研究分支,旨在推断网络中节点对间存在连边的可能性。现实生活中很多事物关系都能够通过网络科学进行描述,很多实际问题都可以转化为链路预测问题。节点无特征网络链路预测算法可向有向网络、加权网络、时序网络等更复杂的网络推广。但现有的链路预测算法面临着网络结构信息挖掘不够深入、特征提取过程受人为主观意识影响、算法很难迁移到其他网络中、算法复杂度过高而无法在大型真实工业网络中应用等诸多问题。针对上述问题,文中基于图注意力网络的基本结构,采用图嵌入表示技术采集节点特征,类比神经图灵机中的内存寻址策略,结合复杂网络重要节点发现的相关工作,设计了一种快速高效的注意力计算方式,提出了一种融合快速注意力机制的节点无特征网络链路预测算法(Faster Attention Mechanism Link Prediction Algorithm,FALP)。在3个公开数据集和1个私有数据集上进行实验,结果表明,FALP算法有效避免了上述问题,同时具有优异的预测性能。
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