A sequence-graph model using gated modulation of methylation signals by eight handcrafted DNA sequence features achieves 3.149 years MAE on 3707 samples, a 12.8% gain over graph baselines.
Mul- timodal learning with graphs.Nature Machine Intelligence, 5(4):340–350
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RoleMAG learns neighbor roles in multimodal graphs to route shared, complementary, and heterophilous signals through separate channels, improving propagation without modality interference.
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
Hybrid knowledge graph embeddings fused with vision transformer features outperform standard techniques on abstract concept classification by integrating situated perceptual knowledge from a new cultural image resource.
citing papers explorer
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Bridging Sequence and Graph Structure for Epigenetic Age Prediction
A sequence-graph model using gated modulation of methylation signals by eight handcrafted DNA sequence features achieves 3.149 years MAE on 3707 samples, a 12.8% gain over graph baselines.
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RoleMAG: Learning Neighbor Roles in Multimodal Graphs
RoleMAG learns neighbor roles in multimodal graphs to route shared, complementary, and heterophilous signals through separate channels, improving propagation without modality interference.
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Explaining Graph Neural Networks for Node Similarity on Graphs
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
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Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification
Hybrid knowledge graph embeddings fused with vision transformer features outperform standard techniques on abstract concept classification by integrating situated perceptual knowledge from a new cultural image resource.