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

扫码查看

原文链接

万方数据
维普

外文摘要：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.

外文关键词：

Semantic communication and computingMulti-layer networkGraph neural networkMotif

作者：

Lu Sun、Xiaona Li、Mingyue Zhang、Liangtian Wan、Yun Lin、Xianpeng Wang、Gang Xu

展开 >

作者单位：

Department of Communication Engineering,Institute of Information Science Technology,Dalian Maritime University,Dalian,116026,China

Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China

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

College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China

School of Information and Communication Engineering,Hainan University,Haikou 570228,China

State Key Laboratory of Millimeter Waves,School of Information Science and Engineering Southeast University,Nanjing 210096,China

展开 >

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Liaoning ProvinceNatural Science Foundation of Liaoning ProvinceKey Laboratory of Big Data Intelligent Computing Funds for Chongqing University of Posts and TelecommunicationsFundamental Research Funds for the Central Universities

项目编号：

6210108861801076619713362022-MS-1572023-MS-108BDIC-2023-A-0033132022230

出版年：

2024

DOI：

10.1016/j.dcan.2024.01.002

数字通信与网络(英文)

ISSN：

年,卷(期)：2024.10(3)