Graph Neural Networks (GNNs) are becoming increasingly popular due to th...
Memory-based Temporal Graph Neural Networks are powerful tools in dynami...
Model pre-training on large text corpora has been demonstrated effective...
How can we learn effective node representations on textual graphs? Graph...
Transparency and accountability have become major concerns for black-box...
Relational graph neural networks (RGNNs) are graph neural networks (GNNs...
Graph Neural Networks (GNNs) are powerful deep learning methods for
Non-...
Can we combine heterogenous graph structure with text to learn high-qual...
This paper describes a new method for representing embedding tables of g...
Many real world graphs contain time domain information. Temporal Graph N...
Graph neural networks (GNN) have shown great success in learning from
gr...
Recent top-k computation efforts explore the possibility of revising
var...
Graph neural networks (GNNs) are powerful tools for learning from graph ...
Graph convolutional network (GCN) based approaches have achieved signifi...
Graph representation learning has made major strides over the past decad...
Graph neural networks (GNN) have shown great success in learning from
gr...
Graph neural networks (GNNs) are gaining increasing popularity as a prom...
Learning unsupervised node embeddings facilitates several downstream tas...
Predicting interactions among heterogenous graph structured data has num...
Knowledge graphs have emerged as a key abstraction for organizing inform...
Graph datasets exceed the in-memory capacity of most standalone machines...
Clustering algorithms are iterative and have complex data access pattern...
Shrinking transistors, which powered the advancement of computing in the...
Classification of individual samples into one or more categories is crit...
Many eigensolvers such as ARPACK and Anasazi have been developed to comp...
Solid state disks (SSDs) have advanced to outperform traditional hard dr...