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Found 9 papers, 5 papers with code
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties.
Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers
These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.
Supervised Attention Using Homophily in Graph Neural Networks
Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks.
What Do GNNs Actually Learn? Towards Understanding their Representations
In recent years, graph neural networks (GNNs) have achieved great success in the field of graph representation learning.
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia
To the best of our knowledge, there is no prior work exploiting a NAS approach and these fusion methods in the task of dementia detection from spontaneous speech.
Explainable Multilayer Graph Neural Network for Cancer Gene Prediction
Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction.
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations
In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs.
Graph Ordering Attention Networks
The GOAT model demonstrates its increased performance in modeling graph metrics that capture complex information, such as the betweenness centrality and the effective size of a node.
Graph-based Neural Architecture Search with Operation Embeddings
Neural Architecture Search (NAS) has recently gained increased attention, as a class of approaches that automatically searches in an input space of network architectures.
