Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc

By Sina Lin, Mingyu Gao, Matei A. Zaharia, A. Aiken. 2020

290 citations recorded by the source corpus.

Subfields: Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Graph Learning

1. Da Zheng
2. Minjie Wang
3. Chao Ma
4. Zheng Zhang
5. Quan Gan
6. George Karypis
7. Jinjing Zhou
8. Joydeep Ghosh

Citations in the corpus

• Pathways: Asynchronous Distributed Dataflow for ML (2022, background): “Graph neural network training (Jia et al., 2020), neural architecture search (Pham et al., 2018), and multi-modal multi-task learning systems (Ma et al., 2018; Lepikhin et al., 2020; Zhao et al., 2019) are examples of inherently heterogeneous and dynamic tasks that do not fit naturally in the SPMD…”
• DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs (2020, background): “Some of them [12]–[14] perform full graph training on multiple GPUs or distributed memory whose aggregated memory fit the graph data.”