Graph structure learning
37 papers with code • 1 benchmarks • 2 datasets
Semi-supervised node classification when a graph structure is not available.
Cora
ChEMBL v.27
Most implemented papers
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
Graph-Bert: Only Attention is Needed for Learning Graph Representations
We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.
DBGSL: Dynamic Brain Graph Structure Learning
Kaleidophon/deep-significance
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Recently, graph neural networks (GNNs) have shown success at learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks.
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.
Deep Iterative and Adaptive Learning for Graph Neural Networks
hugochan/IDGL
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In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
gicheonkang/sglkt-visdial
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Findings (EMNLP) 2021
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
llan-ml/MetaTNE
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NeurIPS 2020
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
DM2-ND/CoEvoGNN
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In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation.
