基于结构深度网络嵌入模型的节点标签分类算法

Node Label Classification Algorithm Based on Structural Depth Network Embedding Model

在海量数据呈现爆炸增长态势的互联网时代,传统算法已无法满足处理大规模、多类型数据的需求。近年来最新的图嵌入算法通过学习图网络特征,在链路预测、网络重构和节点分类实践中普遍取得了极佳的效果。文中基于传统自动编码器模型,创新地提出了一种融合Sdne算法与链路预测相似度矩阵的新算法,通过在反向传播过程中引入高阶损失函数,依据自编码器的新特征调整性能,改进传统算法中以单一方式判定节点相似度这一方法存在的弊端,并建立简易模型分析证明优化的合理性。对比最新研究中效果最好的Sdne算法,该算法在Micro-F1和Macro-F1两种评价指标上的提升效果均接近1%,可视化分类效果表现良好。与此同时,研究发现高阶损失函数超参的最优值大致处于1~10范围内,数值的变化依旧能够基本稳定维持整体网络的鲁棒性。

In the era of Internet,where massive data is growing explosively,traditional algorithms have been unable to meet the needs of processing large-scale and multi type data.In recent years,the latest graph embedding algorithm has achieved excellent results in link prediction,network reconstruction and node classification by learning graph network characteristics.Based on the traditional automatic encoder model,a new algorithm combining Sdne algorithm and link prediction similarity matrix is proposed.By introducing a high-order loss function in the process of back-propagation,the performance is adjusted according to the new characteristics of the auto-encoder.The disadvantages of traditional algorithm in determining node similarity in a single way are improved.A simple model is established to analyze and prove the rationality of the optimization.Compared with the most effective Sdne algorithm in the latest research,the improvement effect of this algorithm on Micro-F1and Macro-F1two evaluation indicators is close to 1%,and the visual classification effect is good.At the same time,it is found that the optimal value of the hyperparameter of the higher-order loss function is approximately in the range of 1~10,and the change of the numerical value can basically maintain the robustness of the whole network.