A Gentle Introduction to Graph Neural Networks Distill About Prize Submit A Gentle Introduction
to Graph Neural Networks Neural networks have been adapted to leverage the structure and …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

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 …

Precise hardware performance models play a crucial role in code optimizations. They can
assist compilers in making heuristic decisions or aid autotuners in identifying the optimal …

Training Graph Neural Networks (GNNs) on large graphs is a fundamental challenge due to
the high memory usage, which is mainly occupied by activations (eg, node embeddings) …

TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It
is designed from the bottom up to support the kinds of rich heterogeneous graph data that …

Sampling subgraphs for training Graph Neural Networks (GNNs) is receiving much attention
from the GNN community. While a variety of methods have been proposed, each method …

As Deep Learning (DL) systems are widely deployed for mission-critical applications,
debugging such systems becomes essential. Most existing works identify and repair …

Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for
the important task of node clustering has not been fully exploited. In particular, most state-of …

Semi-supervised learning is an effective method to study how to use both labeled data and
unlabeled data to improve the performance of the classifier, which has become the hot field …