一种融合伴随信息的网络表示学习模型

Network Representation Learning Model Integrating Accompanying Information

网络表示学习被认为是提高信息网络分析效率的关键技术之一,旨在将网络中每个节点映射为低维隐空间中的向量表示,并使这些向量高效的保持原网络的结构和特性.近年来,大量研究致力于网络拓扑和节点属性的深度挖掘,并在一些网络分析任务中取得了良好应用效果.事实上,在这两类关键信息之外,真实网络中广泛存在的伴随信息,反映了网络中复杂微妙的各种关系,对网络的形成和演化起着重要作用.为提高网络表示学习的有效性,提出了一种能够融合伴随信息的网络表示学习模型NRLIAI.该模型以变分自编码器(VAE)作为信息传播和处理的框架,在编码器中利用图卷积算子进行网络拓扑和节点属性的聚合与映射,在解码器中完成网络的重构,并融合伴随信息对网络表示学习过程进行指导.该模型克服了现有方法无法有效利用伴随信息的缺点,同时具有一定的生成能力,能减轻表示学习过程中的过拟合问题.在真实网络数据集上,通过节点分类和链路预测任务对NRLIAI模型与几种现有方法进行了对比实验,实验结果验证了该模型的有效性.

Network representation learning is regarded as a key technology for improving the efficiency of information network analysis.It maps network nodes to low-dimensional vectors in a latent space and maintains the structure and characteristics of the original network in these vectors efficiently.In recent years,many studies focus on exploring network topology and node features intensively,and the application bears fruit in many network analysis tasks.In fact,besides these two kinds of key information,the accompanying information widely existing in the network reflects various complex relationships and plays an important role in the network’s construction and evolution.In order to improve the efficiency of network representation learning,a novel model integrating the accompanying information is proposed with the name NRLIAI.The model employs the variational auto-encoders(VAE)to propagate and process information.In addition,it aggregates and maps network topology and node features by graph convolutional operators in the encoder,reconstructs the network in the decoder,and integrates the accompanying information to guide the network representation learning.Furthermore,the proposed model solves the problem that the existing methods fail to utilize the accompanying information effectively.At the same time,the model possesses a generative ability,which enables it to reduce the overfitting problem in the learning process.With several real-world network datasets,this study conducts extensive comparative experiments on the existing methods of NRLIAL through node classification and link prediction tasks,and the experimental results have proved the feasibility of the proposed model.