Model pre-training on large text corpora has been demonstrated effective...
Graph Neural Networks (GNNs) have demonstrated promising outcomes across...
How can we learn effective node representations on textual graphs? Graph...
Predicting the responses of a cell under perturbations may bring importa...
Estimating an individual's potential outcomes under counterfactual treat...
Can we combine heterogenous graph structure with text to learn high-qual...
Graph Neural Networks (GNNs) with numerical node features and graph stru...
Knowledge Graph Question Answering (KGQA) involves retrieving facts from...
For supervised learning with tabular data, decision tree ensembles produ...
Consistency training is a popular method to improve deep learning models...
Uncovering anomalies in attributed networks has recently gained populari...
The coronavirus disease (COVID-19) has claimed the lives of over 350,000...
Learning unsupervised node embeddings facilitates several downstream tas...
Predicting interactions among heterogenous graph structured data has num...
The era of "data deluge" has sparked renewed interest in graph-based lea...
Graph convolutional networks (GCNs) have well-documented performance in
...
Graph convolutional networks (GCNs) are vulnerable to perturbations of t...
A graph-based sampling and consensus (GraphSAC) approach is introduced t...
The era of data deluge has sparked the interest in graph-based learning
...
Joint analysis of data from multiple information repositories facilitate...
Network science provides valuable insights across numerous disciplines
i...
The study of networks has witnessed an explosive growth over the past de...
Inference of space-time varying signals on graphs emerges naturally in a...