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With the rapid development of 5th Generation Mobile Communication Technology(5G), the diverse forms of collaboration and extensive data in academic socialnetworks constructed by 5G papers make the management and analysis of academicsocial networks increasingly challenging. Despite the particular successachieved by representation learning in analyzing academic and social networks,most present presentation learning models focus on maintaining the first-orderand second-order similarity of nodes. They rarely possess similar structuralcharacteristics of spatial independence in the network. This paper proposes aLow-order Network representation Learning Model (LNLM) based on Non-negativeMatrix Factorization (NMF) to solve these problems. The model uses the randomwalk method to extract low-order features of nodes and map multiple componentsto a low-dimensional space, effectively maintaining the internal correlationbetween members. This paper verifies the performance of this model, conductscomparative experiments on four test datasets and four real network datasetsthrough downstream tasks such as multi-label classification, clustering, andlink prediction. Comparing eight mainstream network representation learningmodels shows that the proposed model can significantly improve the detectionefficiency and learning methods and effectively extract local and low-orderfeatures of the network.

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