GOMA refines frozen multimodal embeddings via modality-aware graph signal smoothing on attributed graphs to improve retrieval while avoiding over-smoothing.
Hamilton and Jure Leskovec , title =
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R-GFM constructs multi-scale Riemannian graph-of-graphs to learn geometry-adaptive representations, reducing structural domain generalization error and delivering up to 49% relative gains on downstream graph tasks.
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.
citing papers explorer
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GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
GOMA refines frozen multimodal embeddings via modality-aware graph signal smoothing on attributed graphs to improve retrieval while avoiding over-smoothing.
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Learning Graph Foundation Models on Riemannian Graph-of-Graphs
R-GFM constructs multi-scale Riemannian graph-of-graphs to learn geometry-adaptive representations, reducing structural domain generalization error and delivering up to 49% relative gains on downstream graph tasks.
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DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs
DuConTE is a dual-granularity text encoder that incorporates graph topology into language model attention for improved node representations in text-attributed graphs.