JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (7): 84-93.doi: 10.6040/j.issn.1671-9352.4.2024.839

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Hierarchical graph representation learning based on graphical mutual information pooling

WU Xinyao1,2, XU Ji1*   

  1. 1. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou, China;
    2. School of Computer Science and Technology, Guizhou University, Guiyang 550025, Guizhou, China
  • Published:2025-07-01

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Abstract: A graph pooling operator called graphical mutual information pooling(GMIPool). GMIPool is proposed utilizes mutual information neural estimation to measure the mutual information between nodes and their corresponding subgraphs, including both feature mutual information and structural mutual information. It leverages this information to identify and retain key nodes in the graph, constructing a more compact coarsened graph. To ensure structural consistency between the original and coarsened graphs, the method adjusts the coarsened graphs structure using the neighborhood correlations between nodes. Experiments on several node classification task datasets validate the effectiveness of GMIPool.

Key words: graph neural networks, graph pooling, multi-granularity, graphical mutual information, mutual information neural estimation

CLC Number: 

  • TP391
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