Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (1): 58-76.doi: 10.11896/jsjkx.250300081

• Database & Big Data & Data Science • Previous Articles     Next Articles

Review of Graph Embedding Learning Research:From Simple Graph to Complex Graph

HUANG Miaomiao1, WANG Huiying2, WANG Meixia1, WANG Yejiang1 , ZHAO Yuhai1   

  1. 1 School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
    2 Information and Communication Branch, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110004, China
  • Received:2025-03-17 Revised:2025-06-09 Published:2026-01-08
  • About author:HUANG Miaomiao,born in 1999,Ph.D.Her main research interests include graph learning and drug discovery.
    ZHAO Yuhai,born in 1975,Ph.D,professor.His main research interests include data mining and machine lear-ning.
  • Supported by:
    National Natural Science Foundation of China(62432003,92267206,62032013).

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Abstract: Graph data,as a data type with strong expressive power,is difficult to model efficiently due to its complex structure.How to effectively capture its intrinsic information has become a challenging problem.Graph embedding methods have received increasing attention by mapping high-dimensional sparse graphs into low-dimensional dense feature vectors,while preserving the structural information of graphs.However,the existing reviews do not summarize the graph embedding methods comprehensively enough,especially paying less attention to complex graph embedding,which leads to the failure to systematically sort out the current status of research on graph embedding in dealing with diverse graph data.Therefore,this paper presents a systematic review of graph embedding learning methods from simple to complex graphs.Firstly,it gives the common definitions of various types of graphs and graph embedding.Secondly,it systematically summarizes the embedding methods on simple graphs,including shallow and deep embedding methods.Then,it summarizes the embedding methods on complex graphs according to the types of graphs,focusing on the application of deep embedding techniques in complex graph structures such as dynamic graphs,heterogeneous graphs,multiplex graphs,and hypergraphs,to fill the gaps in the existing literature that is insufficiently researched on complex graph structures.Finally,it discusses the practical application scenarios of graph embedding techniques,and looks forward to the future development directions.

Key words: Graph embedding, Graph representation, Deep learning, Neural network, Complex graph

CLC Number: 

  • TP181
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