基于增量学习的社交网络链路预测  

Social Networks Link Prediction Based on Incremental Learning

作　　者：舒坚[1] 陈芷晨 SHU Jian;CHEN Zhichen(School of Software,Nanchang Hangkong University,Nanchang 330063,China;School of Future Technology,Jiangxi Institute of Applied Science and Technology,Nanchang 330100,China)

机构地区：[1]南昌航空大学软件学院,江西南昌330063 [2]江西应用科技学院未来技术学院,江西南昌330100

出　　处：《工程科学与技术》2025年第2期1-11,共11页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(62362052,62062050);江西省教育厅科学技术研究项目(GJJ2403207);江西省研究生创新专项基金项目(YC2022-S764)。

摘　　要：社交网络中,节点间存在多种关系类型,节点数量会随着时间的推移而变化,这种异质性和动态性给链路预测任务带来极大的挑战。因此,本文提出一种基于增量学习的社交网络链路预测方法(incremental learning social networks link prediction,IL-SNLP)。通过对网络进行分层,使每一层网络只包含一种关系类型,以更好地获取节点在每种关系类型下的语义信息;针对网络的动态性,利用时序随机游走捕获社交网络中的局部结构信息和时序信息;针对增量数据,采用增量式更新随机游走策略对历史随机游走序列进行更新。通过增量式skip-gram模型从随机游走序列中提取新出现节点的特征,并进一步更新历史节点的特征;针对网络的异质性,采用概率模型提取不同关系类型之间的因果关系关联程度,并将其作用于每一层的节点特征,以改善不同关系层下节点特征表现能力;利用多层感知机构建节点相互感知器,挖掘节点间建立连接时的相互贡献,实现更高的链路预测准确率。实验结果表明,在3个真实的社交网络数据集上,IL-SNLP方法的ROC曲线下的面积(AUC)和F1分数比基线方法分别提高了10.08%~67.60%和1.76%~64.67%,提升了预测性能;对于增量数据,只需要少次迭代就能保持预测模型的性能,提高了模型训练的速度;与未采用增量学习技术的IL-SNLP−方法相比,IL-SNLP方法在时间效率上提升了30.78%~257.58%,显著缩短了模型的运行时长。Objective Various types of relationships in social networks exist between nodes,and the number of nodes changes over time.The heterogeneity and dynamic topology pose significant challenges for link prediction.Specifically,this study addresses three key issues:(1)an incremental update strategy for random walk sequences,(2)the extraction of the correlation of causal relationships,and(3)the construction of the mutual perceptron.Methods Therefore,an incremental learning social network link prediction(IL−SNLP)method is proposed.IL−SNLP consists of two components:the node embedding model and the prediction model.The node embedding model structures the network in layers based on relationship types.An incremental update strategy is designed for each network layer to generate updated random walk sequences,employing a temporal random walk approach.The incremental skip-gram model is then utilized to extract the embedding vectors of nodes from the random walk sequences in each layer.A probabilistic model is employed to extract the correlation of causal relationships between relationship types,enhancing the representation of nodes across different layers.In the prediction model,a multilayer perceptron(MLP)is utilized to construct the mutual perceptron,which predicts links by processing the embedding vectors of node pairs.The specific process of the node embedding model consists of temporal random walks,an incremental update strategy for random walk sequences,an incremental skip-gram model,and extracting the correlation of causal relationships.In the process of generating random walk sequences based on temporal random walks,the next node is selected if its timestamp is greater than the current timestamp.This approach ensures that both local structural and temporal information are preserved within these sequences.An incremental update strategy is introduced to update the random walk sequences in scenarios involving data increments.This updated strategy considers three situations:completely outdated sequences,partially outda

关 键 词：社交网络 链路预测 增量学习 时序随机游走 概率模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]