LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm  

作　　者：Shihu Liu Chunsheng Yang Yingjie Liu 

机构地区：[1]School of Mathematics and Computer Science,Yunnan Minzu University,Kunming,650504,China

出　　处：《Intelligent Automation & Soft Computing》2023年第12期203-223,共21页智能自动化与软计算（英文）

基　　金：What is more,we thank the National Natural Science Foundation of China(Nos.61966039,62241604);the Scientific Research Fund Project of the Education Department of Yunnan Province(No.2023Y0565);Also,this work was supported in part by the Xingdian Talent Support Program for Young Talents(No.XDYC-QNRC-2022-0518).

摘　　要：Many network presentation learning algorithms(NPLA)have originated from the process of the random walk between nodes in recent years.Despite these algorithms can obtain great embedding results,there may be also some limitations.For instance,only the structural information of nodes is considered when these kinds of algorithms are constructed.Aiming at this issue,a label and community information-based network presentation learning algorithm(LC-NPLA)is proposed in this paper.First of all,by using the community information and the label information of nodes,the first-order neighbors of nodes are reconstructed.In the next,the random walk strategy is improved by integrating the degree information and label information of nodes.Then,the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model.At last,the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification,network reconstruction and link prediction tasks,compared with three benchmark algorithms.

关 键 词：Label information community information network representation learning algorithm random walk 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]