Search Results for author:
Found 9 papers, 2 papers with code
DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees
We propose the fully explainable Decision Tree Graph Neural Network (DT+GNN) architecture.
Asynchronous Neural Networks for Learning in Graphs
This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs.
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs.
Ranked #11 on
Graph Classification
on IMDb-B
Should Graph Neural Networks Use Features, Edges, Or Both?
We find that for graph classification, a GNN is not more than the sum of its parts.
Towards Robust Graph Contrastive Learning
We study the problem of adversarially robust self-supervised learning on graphs.
Contrastive Graph Neural Network Explanation
Graph Neural Networks achieve remarkable results on problems with structured data but come as black-box predictors.
Learning Lower Bounds for Graph Exploration With Reinforcement Learning
Given there are quadratically many possible edges in a graph and each subset of edges is a possible solution, this yields unfeasibly large search spaces even for few nodes.
Medley2K: A Dataset of Medley Transitions
The automatic generation of medleys, i. e., musical pieces formed by different songs concatenated via smooth transitions, is not well studied in the current literature.
Neural Status Registers
Standard Neural Networks can learn mathematical operations, but they do not extrapolate.
