November 21, 2025Intelligent Data Analysis

Structural entropy based graph contrastive learning for node classification

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Authors

JZJun Zhang LDLiang Duan XCXiang Chen

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Implication

This approach enhances feature learning and improves robustness in node classification via graph neural networks, suggesting effective label handling.

Key Points

Robustness in node classification improves through structural entropy-based graph contrastive learning.
This method enhances feature learning by using graph neural networks and attention mechanisms during training.
Graph contrastive learning is deployed to effectively manage label scarcity in node classification tasks.
The approach may support better classification by integrating community structures, enhancing overall model effectiveness.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/6924e3ffc0ce034ddc34f548 https://doi.org/10.1177/1088467x251395501

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Implication

This approach enhances feature learning and improves robustness in node classification via graph neural networks, suggesting effective label handling.

Structural entropy based graph contrastive learning for node classification

Discussion

Member takes

Implication

Key Points

Cite This Study

Structural entropy based graph contrastive learning for node classification

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider