arXiv Machine Learning
Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation
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NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning
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Handling Feature Heterogeneity with Learnable Graph Patches
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Semi-Supervised Text-Attributed Graph Distillation
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Node4All: Learning Node Representation Beyond Datasets
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Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.
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Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
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