Search Results for author:
Found 9 papers, 5 papers with code
Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks
A surge of interest in Graph Convolutional Networks (GCN) has produced thousands of GCN variants, with hundreds introduced every year.
TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow.
Synthetic Graph Generation to Benchmark Graph Learning
This shockingly small sample size (~10) allows for only limited scientific insight into the problem.
Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks
We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.
GraphWorld: Fake Graphs Bring Real Insights for GNNs
Using GraphWorld, a user has fine-grained control over graph generator parameters, and can benchmark arbitrary GNN models with built-in hyperparameter tuning.
Recurrent Graph Neural Networks for Rumor Detection in Online Forums
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content.
Finding Stable Groups of Cross-Correlated Features in Two Data Sets With Common Samples
Given two data types, a bimodule is a pair $(A, B)$ of feature sets from the two types such that the aggregate cross-correlation between the features in $A$ and those in $B$ is large.
Graph Clustering with Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.
MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit
In this paper, we show that when metadata is correlated with the formation of node neighborhoods, unsupervised node embedding dimensions learn this metadata.
