Analysis of 5G academic Network based on graph representation learning method

TL;DR

This paper introduces LNLM, a novel network representation learning model based on NMF, which effectively captures low-order features in 5G academic social networks, improving analysis tasks like classification and link prediction.

Contribution

The paper proposes a new low-order network representation learning model using NMF and random walk, addressing structural independence issues in existing models.

Findings

LNLM outperforms eight mainstream models in efficiency and feature extraction.

The model improves accuracy in multi-label classification, clustering, and link prediction.

Experimental results demonstrate the effectiveness of LNLM on multiple datasets.

Abstract

With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by 5G papers make the management and analysis of academic social networks increasingly challenging. Despite the particular success achieved by representation learning in analyzing academic and social networks, most present presentation learning models focus on maintaining the first-order and second-order similarity of nodes. They rarely possess similar structural characteristics of spatial independence in the network. This paper proposes a Low-order Network representation Learning Model (LNLM) based on Non-negative Matrix Factorization (NMF) to solve these problems. The model uses the random walk method to extract low-order features of nodes and map multiple components to a low-dimensional space, effectively maintaining the internal correlation between members. This paper verifies the performance of this model, conducts comparative experiments on four test datasets and four real network datasets through downstream tasks such as multi-label classification, clustering, and link prediction. Comparing eight mainstream network representation learning models shows that the proposed model can significantly improve the detection efficiency and learning methods and effectively extract local and low-order features of the network.