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 …
Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
Deep graph library: A graph-centric, highly-performant package for graph neural networks
Advancing research in the emerging field of deep graph learning requires new tools to
support tensor computation over graphs. In this paper, we present the design principles and …
support tensor computation over graphs. In this paper, we present the design principles and …
Deep graph library: Towards efficient and scalable deep learning on graphs
MY Wang - ICLR workshop on representation learning on graphs …, 2019 - par.nsf.gov
Advancing research in the emerging field of deep graph learning requires new tools to
support tensor computation over graphs. In this paper, we present the design principles and …
support tensor computation over graphs. In this paper, we present the design principles and …
Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs
to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical …
to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical …
Dorylus: Affordable, scalable, and accurate {GNN} training with distributed {CPU} servers and serverless threads
A graph neural network (GNN) enables deep learning on structured graph data. There are
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …
two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which …
P3: Distributed deep graph learning at scale
Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …
become one of the fastest growing subareas in deep learning. While several new GNN …
Pagraph: Scaling gnn training on large graphs via computation-aware caching
Emerging graph neural networks (GNNs) have extended the successes of deep learning
techniques against datasets like images and texts to more complex graph-structured data …
techniques against datasets like images and texts to more complex graph-structured data …
ByteGNN: efficient graph neural network training at large scale
Graph neural networks (GNNs) have shown excellent performance in a wide range of
applications such as recommendation, risk control, and drug discovery. With the increase in …
applications such as recommendation, risk control, and drug discovery. With the increase in …