May 23, 2024 · The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments ...
Sep 6, 2024 · The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments ...
May 23, 2024 · In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis.
Nov 5, 2024 · In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis.
The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the.
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning. Run. python main.py --use_sage=HCGAE --dataset=Cora --epochs=100 ...
The paper introduces a new model called HC-GAE for analyzing graphs (collections of points connected by lines) effectively. This model improves how we ...
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HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning · 23 May 2024 ; ENADPool: The Edge-Node Attention-based Differentiable ...
Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), ...
IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 5528014, 2023. HC-GAE: The hierarchical cluster-based graph auto-encoder for graph representation ...