9 datasets found

The ogbn-papers100M dataset is a benchmark for graph convolutional networks.

Graph Neural Networks (GNNs) have become a popular approach for various applications, ranging from social network analysis to modeling chemical properties of molecules.

The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally.

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains.

Simplifying graph convolutional networks.

Graph Convolutional Networks (GCNs) with multi-hop aggregation is more expressive than one-hop GCN but suffers from higher model complexity. Finding the shortest aggregation...

The dataset used in the paper to evaluate the influence of graph elements on the parameter changes of GCNs without needing to retrain the GCNs.

The dataset used in the paper to evaluate the influence of graph elements on the parameter changes of GCNs without needing to retrain the GCNs.

The dataset used in the paper to evaluate the influence of graph elements on the parameter changes of GCNs without needing to retrain the GCNs.