文献类型：期刊文献

中文题名：基于深度学习和多特征融合的时序社交网络关键节点识别

英文题名：Key Node Identification in Temporal Social Networks Based on Deep Learning and Multi-feature Fusion

作者：张雪芹[1,2];王智能[1];李晋生[1];陆一松[1];罗飞[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]上海市计算机软件评测重点实验室,上海201112

年份：2026

卷号：53

期号：4

起止页码：143

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：;北大核心:【北大核心2023】；

基金：国家社会科学基金重大项目(23&ZD142)。

语种：中文

中文关键词：时序社交网络;节点影响力;网络表示;时空特征;深度学习

外文关键词：Temporal social network;Node influence;Network representation;Spatio-temporal characteristics;Deep learning

摘要：社交网络是信息传播的主要渠道,识别社交网络中的关键节点对发现信息传播枢纽、进行信息传播控制等具有重要意义。现实社交网络具有时变性,合理建模时序网络,并对节点的空间和时间关系进行全面描述和深度挖掘,是准确识别网络关键节点的重要因素。为了提高关键节点识别的精度,提出了一种基于深度学习和多特征融合的时序社交网络关键节点识别方法MCNN(Multidimensional CNN)。该方法首先将时序网络建模为基于快照的多维关系网络,对于一个节点,在每个快照,分别从空间结构、时间耦合和三类时空传播关系,提取节点的空间、时间和时空上下文,并构建节点特征矩阵。为了深度分析节点在每个快照中的时空关系,使用卷积神经网络CNN分别提取3类节点特征,并使用自注意力机制融合形成节点快照特征。为了捕捉节点行为在快照间的演变,组合所有快照的节点快照特征作为时间序列,采用长短期记忆网络LSTM挖掘快照序列特征。最后,使用全连接层预测节点的影响力。在6个真实时序社交网络上的实验结果表明,MCNN在时序社交网络关键节点识别方面优于基线方法。
Social network is the main channel of information dissemination,and identifying key nodes in social networks is important for discovering information dissemination hubs and performing information dissemination control.Realistic social networks are time-varying,and reasonable modeling of temporal networks with comprehensive description and deep mining of the spatial and temporal relationships of nodes is an important factor for accurately identifying key nodes in the network.In order to improve the accuracy of key node identification,a deep learning and multi-feature fusion based method MCNN(Multidimensional CNN)for key node identification in temporal social networks is proposed.The method firstly models the temporal network as a multidimensional relational network based on snapshots,and for a node,in each snapshot,the spatial,temporal,and spatio-temporal contexts of the node are extracted from the spatial structure,temporal coupling,and three types of spatio-temporal propagation relations,respectively,and the node feature matrix is constructed.In order to deeply analyze the spatio-temporal relationships of nodes in each snapshot,three types of node features are extracted using CNN,respectively,and fused to form the node snapshot features using the self-attention mechanism.To capture the evolution of node behaviors between snapshots,node snapshot features of all snapshots are combined as a time sequence,and LSTM is used to mine the snapshot sequence features.Finally,the influence of nodes is predicted using a fully connected layer.Experiments on six real temporal social networks show that MCNN outperforms the baseline approaches for key node identification in temporal social networks.