Multi-layer network embedding on scc-based network with motif  

作　　者：Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu 

机构地区：[1]Department of Communication Engineering,Institute of Information Science Technology,Dalian Maritime University,Dalian,116026,China [2]Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [3]Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province,DUT School of Software Technology&DUT-RU International School of Information Science and Engineering,Dalian University of Technology,Dalian 116620,China [4]College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China [5]School of Information and Communication Engineering,Hainan University,Haikou 570228,China [6]State Key Laboratory of Millimeter Waves,School of Information Science and Engineering,Southeast University,Nanjing 210096,China

出　　处：《Digital Communications and Networks》2024年第3期546-556,共11页数字通信与网络（英文版）

基　　金：supported by National Natural Science Foundation of China(62101088,61801076,61971336);Natural Science Foundation of Liaoning Province(2022-MS-157,2023-MS-108);Key Laboratory of Big Data Intelligent Computing Funds for Chongqing University of Posts and Telecommunications(BDIC-2023-A-003);Fundamental Research Funds for the Central Universities(3132022230).

摘　　要：Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关 键 词：Semantic communication and computing Multi-layer network Graph neural network MOTIF 

分 类 号：TN92[电子电信—通信与信息系统]