Embedding graph nodes into a vector space can allow the use ofmachine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs. We examine the performance of node embedding algorithms with respect to graph centrality measures that characterize diverse graphs, through syst...

Graph embedding is an e‚ective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which o‰en results in inferior embedding in real-worl...

Abstract The development of the Internet and big data has led to emergence graphs as an important representation structure in various real-world scenarios. However, size increases, computational complexity memory requirements pose significant challenges for graph embedding. To address this challenge, paper proposes a multilevel embedding refinement framework (MERIT) based on large-scale graphs,...

Recently graph auto-encoders have received increasingly widespread attention as one of the important models in field deep learning. Existing auto-encoder only use convolutional neural networks (GCNs) encoders to learn embedding representation nodes. However, GCNs are suitable for transductive learning, poor scalability and shallow with a perceptual field, limitations node feature extraction. To...

Towards exploring the topological structure of data, numerous graph embedding clustering methods have been developed in recent years, none them takes into account cluster-specificity distribution nodes representations, resulting suboptimal performance. Moreover, most existing execute representations learning and two separated steps, which increases instability its original Additionally, rare si...