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基于多演化特征的社交网络链路预测算法

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社交网络链路预测旨在根据已知的网络信息预测未来的链接关系,在推荐系统和合著网络中具有重要作用.然而,现有链路预测算法往往忽视社交网络的多元演化特点,训练时间复杂度较高,限制其执行效率.针对上述问题,文中提出基于多演化特征的社交网络链路预测算法(Multi-evolutionary Features Based Link Prediction Algo-rithm for Social Network,MEF-LP).首先,设计一种简单高效的时间极限学习机模型,利用门控网络和极限学习机自编码器传递与聚合社交网络快照序列的时间信息.然后,构建多个深度极限学习机,对时间特征进行多角度映射,挖掘社交网络不同的演化特征,并最终融合成综合演化特征.最后,使用基于极限学习机的分类器完成链路预测.在6个真实社交网络上的实验表明,MEF-LP能合理学习社交网络的多演化特征,并获得较优的预测性能.
Multi-evolutionary Features Based Link Prediction Algorithm for Social Network
Social network link prediction aims to predict future link relationships based on known network information,in which there are important applications for recommender systems and co-authorship networks.However,existing link prediction algorithms often ignore multi-evolutionary features of social networks and have high training time complexity,limiting their execution efficiency and application performance.To address these problems,a multi-evolutionary features based link prediction algorithm for social network(MEF-LP)is proposed.Firstly,a simple and efficient time extreme learning machine model is designed to transfer and aggregate the temporal information of social network snapshot sequences,using gated networks and extreme learning machine self-encoders.Secondly,multiple multilayer extreme learning machines are constructed to map temporal features from multiple perspectives,mining different evolutionary features of social networks and ultimately integrating them into comprehensive evolutionary features.Finally,the extreme learning machine-based classifiers are utilized to complete the link prediction.Experiments on six real social networks show that MEF-LP can reasonably learn the multi-evolution features of social networks and achieve better prediction performance.

Social Network AnalysisLink PredictionMultiple EvolutionNetwork SnapshotExtreme Learning Machine

何玉林、赖俊龙、崔来中、黄哲学、尹剑飞

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人工智能与数字经济广东省实验室(深圳) 深圳 518107

深圳大学大数据技术与应用研究所 深圳 518060

社交网络分析 链路预测 多元演化 网络快照 极限学习机

广东省自然科学基金面上项目广东省基础与应用基础研究基金粤深联合基金重点项目深圳市基础研究面上项目深圳市科技重大专项项目

2023A15150116672023B1515120020JCYJ20210324093609026202302D074

2024

10.16451/j.cnki.issn1003-6059.202407003

模式识别与人工智能
中国自动化学会,国家智能计算机研究开发中心,中国科学院合肥智能机械研究所

模式识别与人工智能

CSTPCD北大核心
影响因子：0.954
ISSN：1003-6059
年,卷(期)：2024.37(7)