Search Results

Node-Level Differentially Private Graph Neural Networks

1 code implementation23 Nov 2021

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.

Privacy Preserving

Tackling Provably Hard Representative Selection via Graph Neural Networks

1 code implementation20 May 2022

Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset.

Active Learning Data Compression +1

Do We Need Anisotropic Graph Neural Networks?

2 code implementations3 Apr 2021

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.

Graph Neural Network

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

1 code implementation9 Apr 2019

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.

General Classification Node Classification +1

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

1 code implementation NeurIPS 2020

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.

Code Completion Learning to Execute +2

Graph Neural Networks: A Review of Methods and Applications

5 code implementations20 Dec 2018

Lots of learning tasks require dealing with graph data which contains rich relation information among elements.

Graph Attention Graph Neural Network

How Powerful are Graph Neural Networks?

19 code implementations ICLR 2019

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

General Classification Graph Classification +3

Position-aware Graph Neural Networks

2 code implementations11 Jun 2019

Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs.

Community Detection Graph Neural Network +2

Gated Graph Sequence Neural Networks

13 code implementations17 Nov 2015

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

Drug Discovery Graph Classification +2

Towards Deeper Graph Neural Networks

3 code implementations18 Jul 2020

Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.

Attribute Graph Neural Network +3