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HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning

arXiv 2405.14742 ↗First posted 23 May 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 23 May 2024.

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31 of 48 references.

References · 31

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19 more
  1. 2017graph2vec: Learning Distributed Representations of Graphscited by 228
  2. 2022How Powerful are Spectral Graph Neural Networkscited by 169
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  18. 2022Image Classification using Graph Neural Network and Multiscale Wavelet Superpixelscited by 10
  19. 2021A Robust and Generalized Framework for Adversarial Graph Embeddingcited by 8

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