Abstract: Network embedding as network representation learning has received a lot of attention from researchers in recent years.A number of models based on low-dimensional vector representation of nodes in network structure learning networks,such as DeepWalk,have been developed with good results in tasks such as node classification and link prediction.However,with the network size increases,there are computational bottlenecks with multiple network embedding algorithms.To mitigate this problem,no-learning methods such as random projection can be used,but critical information about the network structure may be lost,resulting in degraded algorithm performance.In this paper,a post-processing algorithm for network embedding(PPNE) is proposed,which uses random projection as well as principal component analysis to effectively retain key information and maintain a higher order approximation of the network structure.Experiments are conducted on three public datasets for node classification and link prediction tasks,while the performance of the PPNE algorithm is verified against other network embedding algorithms.The experimental results show that the PPNE algorithm has a large improvement over other algorithms in terms of both perfor-mance and running time,and the algorithm has a speed improvement of at least two orders of magnitude over other learning-based algorithms while ensuring good task performance.

Key words: Link prediction, Network embedding, Node classification, Principal component analysis, Random projection