Search Results
Node-Level Differentially Private Graph Neural Networks
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node.
Tackling Provably Hard Representative Selection via Graph Neural Networks
Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset.
Do We Need Anisotropic Graph Neural Networks?
We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward.
Ranked #11 on
Graph Property Prediction
on ogbg-code2
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.
Ranked #26 on
Node Classification
on Citeseer
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
More practically, we evaluate these models on the task of learning to execute partial programs, as might arise if using the model as a heuristic function in program synthesis.
Graph Neural Networks: A Review of Methods and Applications
Lots of learning tasks require dealing with graph data which contains rich relation information among elements.
How Powerful are Graph Neural Networks?
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Ranked #1 on
Graph Classification
on COX2
Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.
Gated Graph Sequence Neural Networks
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
Ranked #1 on
Graph Classification
on IPC-grounded
Towards Deeper Graph Neural Networks
Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
Ranked #4 on
Node Classification
on AMZ Computers
