融合快速注意力机制的节点无特征网络链路预测算法

Link Prediction for Node Featureless Networks Based on Faster Attention Mechanism

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。

Link prediction is an important task in network science.It aims to predict the link existence probabilities of two nodes.There are many relations between substances in real word,which can be described by network science in computers.There are many problems of daily life,which can be transformed to link prediction tasks.Link prediction algorithms for node featureless networks are convenient to migrate in directed networks,weighted networks,time networks,and so on.However,the traditional link prediction algorithms are faced with many problems as follows.The network structures information mining is not deep enough.The feature extraction processes depend on subjective consciousness.The algorithms are short of universality,and the time complexity and space complexity are flawed,which cause that they are difficult to be applied to real industry networks.In order to effectively avoid the above problems,based on the basic structure of graph attention network,graph embedding representation technology is used to collect node characteristics,analogy with the memory addressing strategy in neural turing machine,and combined with the relevant work of important node discovery in complex network,a fast and efficient attention calculation method is designed,and a node featureless network link prediction algorithm FALP integrating fast attention mechanism is proposed.Experiment on three public datasets and a private dataset show that the FALP effectively avoids these problems and has excellent predictive performance.