Papers with Graph representation learning
GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
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| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
Graph Representation Learning in Hyperbolic Space via Dual-Masked (2025.coling-main)
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| Challenge: | Existing MR-based methods do not fully consider deep node and structural information. |
| Approach: | They propose a graph dual-masked self-supervised graph representation learning framework in hyperbolic space that masks nodes and edges and performs node aggregation. |
| Outcome: | The proposed method is superior in downstream tasks such as node classification and link prediction. |