摘要
机会网络是一种利用节点移动带来相遇机会实现通信的自组织网络,可应用于野生动物追踪、水下营救、偏远地区网络覆盖等场景。相较于普通网络,机会网络具有节点连接稀疏、网络结构复杂及拓扑变化频繁等特点,给机会网络带来挑战。本文提出一种基于特征相似性的机会网络链路预测模型(FS-ET)。首先,采用Graph2vec模型表征网络得到其样本熵和模型训练时长,基于二者选取合适的切片时长,将动态网络进行切片生成离散的网络演化序列。然后,采用基于GraphSAGE的节点嵌入模型提取节点潜在特征,并结合节点的连接时长计算节点对的潜在特征相似性;通过分析节点与多阶邻居的连边关系,根据节点局部连接信息计算节点对的拓扑结构相似性;采用L2范数对潜在特征相似性及拓扑结构相似性进行融合。最后,改进趋势移动平均法对历史信息进行时序特征提取,并提取融合后节点相似性的时序特征,得到未来节点间产生连接的可能性。在ITC、MIT、Infocom05和Infocom06共4个真实机会网络数据集上进行了切片、模型复杂度、泛化性、有效性的实验。结果表明:与长短期记忆网络模型(LSTM)、网络嵌入补充网络域中相似性模型(NESND)、端到端链路预测模型(E-LSTM-D)、非线性链路预测模型(GCN-GAN)、结构嵌入门控循环单元的预测模型(SE-GRU)、一种新的端到端链路预测模型(GC-LSTM)等相比,本文提出的FS-ET模型提升了0.5%~24.8%的AUC和1.07%~22.77%的F1-score,相较于其他基线模型均有一定程度的提升,验证了本文模型具有良好的预测性能。
Objective An opportunistic network is a self-organizing network in which communication arises from the chances of node movement encounters.It can be applied in various scenarios,such as wildlife tracking,underwater rescue expeditions,and network coverage in remote areas.Link prediction involves determining whether a link is missing between two nodes or predicting whether a link will exist in the future,and this study aims to predict future links.Unlike ordinary networks,an opportunistic network is characterized by sparse node connections,complex structures,and frequent topology changes,leading to challenges in link prediction.Most existing opportunistic network link prediction methods employ network embedding algorithms for link prediction,but these algorithms have poor prediction effectiveness in networks with short average paths,and they do not fully utilize the network’s local structure information.Therefore,this study proposes a link prediction model for opportunistic networks based on feature similarity.The similarity of nodes is defined by considering node embedding vectors and local node information to improve the link prediction effect in opportunistic networks.Methods The Graph2vec model was employed to extract features of each snapshot.The sample entropies of the network,varying with candidate slicing slots,were obtained based on the network sample entropy calculation method.The slicing slot was determined using TOPSIS(technique for order preference by similarity to an ideal solution),which scores the candidate slicing slots by considering sample entropies and the training time of the Graph2vec model.The opportunistic network was sliced to generate a sequence of discrete network snapshots.The node embedding model based on GraphSAGE was utilized to extract the latent features of nodes.Considering that the connection duration of the nodes in the current snapshot influences the latent feature similarity of the nodes,the latent feature similarity was calculated by combining the Pearson correlation coefficient and the connection duration.The potential feature of the nodes alone was insufficient for extracting the features of network evolution.Therefore,the local structure information of the nodes was also considered.The topological similarity between nodes was obtained by evaluating the degree of nodes,the distance between nodes,and the number of paths between nodes.The similarity of nodes in each snapshot was achieved by fusing the latent feature similarity and topological similarity using the L2 norm.The traditional trend-moving average method was enhanced by assigning a decayed factor to each snapshot,ensuring that the latest snapshot had the greatest weight.The possibility of a connection between nodes at the next step was obtained using the improved trend-moving average method.Results and Discussions Experiments were conducted on four real opportunistic network datasets,ITC,MIT,Infocom05,and Infocom06,where the dataset information included the number of nodes and the collection time of the datasets.AUC(area under the receiver operating characteristic curve)and F1-score were used as evaluation indicators of model performance.The experiments were divided into slicing slot,model complexity,model generalization,comparative experiment,and ablation experiment phases.Based on the experiments on network sample entropy,Graph2vec model training time,TOPSIS comprehensive score,and AUC value under different slicing slots,it was found that when the slicing slots are 8,10,3,and 6 minutes for datasets ITC,MIT,Infocom05,and Infocom06 respectively,the comprehensive evaluation score curve reaches a stable trend,and AUC achieves a good effect.Therefore,8,10,3,and 6 minutes were selected as the slicing slots for the ITC,MIT,Infocom05,and Infocom06 datasets.The complexity of the model was compared and analyzed from the perspectives of model complexity,including the proposed model and baseline models,as well as the FLOPs and Params of the models.In addition,the prediction time of the models was compared.The accuracy of the proposed method was tested using ten-fold cross-validation.On the ITC,MIT,Infocom05,and Infocom06 datasets,the average AUC of the proposed method exceeded 0.92,with the average AUC on the Infocom05 dataset exceeding 0.95,demonstrating that the model has good generalization.After removing the node topological similarity module in the model for ablation experiments,the AUC of the proposed model decreased by 1.07%to 3.86%,and the F1-score reduced by 6.12%to 15.48%.This validates the effectiveness of the node topology similarity module.Compared to the baseline methods LSTM(long short-term memory),NESND(network embedding supplementing similarity information in the network domain),E-LSTM-D(encoder-LSTM-decoder),GCN-GAN(graph convolutional network-generative adversarial network),SE-GRU(structure embedded gated recurrent unit neural networks),and GC-LSTM(graph convolution embedded LSTM),the AUC improved by 0.5%to 24.8%,and the F1-score increased by 1.15%to 22.77%.Therefore,the proposed model achieves better performance.Conclusions This study proposes a slicing method based on network sample entropy and model training time,as well as an opportunistic network link prediction model,FS-ET,which is based on feature similarity.The experimental results on four real datasets show that,compared to baseline models,FS-ET achieves better performance.
作者
刘琳岚
唐家威
朱文俊
LIU Linlan;TANG Jiawei;ZHU Wenjun(School of Information Engineering,Nanchang Hangkong University,Nanchang 330063,China;School of Software,Nanchang Hangkong University,Nanchang 330063,China)
出处
《工程科学与技术》
北大核心
2025年第2期12-21,共10页
Advanced Engineering Sciences
基金
国家自然科学基金项目(62362052,62062050)。
作者简介
刘琳岚(1966-),女,教授.研究方向:无线传感网络等.E-mail:liulinlan@nchu.edu.cn。
