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Current progress and open challenges for applying deep learning across the biosciences
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand
challenges in computational biology: the half-century-old problem of protein structure …
challenges in computational biology: the half-century-old problem of protein structure …
The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …
neural network architecture is capable of processing graph structured data and bridges the …
Data augmentation for deep graph learning: A survey
Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …
demonstrated remarkable performance on numerous graph learning tasks. To address the …
Graphmae2: A decoding-enhanced masked self-supervised graph learner
Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …
offers great potential to address the fundamental challenge of label scarcity in real-world …
Graph contrastive learning with augmentations
Generalizable, transferrable, and robust representation learning on graph-structured data
remains a challenge for current graph neural networks (GNNs). Unlike what has been …
remains a challenge for current graph neural networks (GNNs). Unlike what has been …
NAGphormer: A tokenized graph transformer for node classification in large graphs
The graph Transformer emerges as a new architecture and has shown superior
performance on various graph mining tasks. In this work, we observe that existing graph …
performance on various graph mining tasks. In this work, we observe that existing graph …
Gpt-gnn: Generative pre-training of graph neural networks
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-
structured data. However, training GNNs requires abundant task-specific labeled data …
structured data. However, training GNNs requires abundant task-specific labeled data …
Heterogeneous graph transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for
modeling structured data. However, most GNNs are designed for homogeneous graphs, in …
modeling structured data. However, most GNNs are designed for homogeneous graphs, in …
Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in
recent years, revealing the suspiciousness of nodes by aggregating their neighborhood …
recent years, revealing the suspiciousness of nodes by aggregating their neighborhood …
Decoupling the depth and scope of graph neural networks
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the
graph and model sizes. On large graphs, increasing the model depth often means …
graph and model sizes. On large graphs, increasing the model depth often means …