Deep Graph Structure Learning for Robust Representations

A Survey on Graph Structure Learning: Progress and Opportunities

Therefore, noisy or incomplete graphs often lead to unsatisfactory representations and prevent us from fully understanding the mechanism ...

Deep Graph Structure Learning for Robust Representations: A Survey

A Survey on Graph Structure Learning: Progress and Opportunities ... Graphs are widely used to describe real-world objects and their interactions.

Deep Graph Structure Learning for Robust Representations: A Survey

A general paradigm of Graph Structure Learning is formulated, and state-of-the-art methods classed by how they model graph structures are reviewed, ...

Deep Graph Structure Learning for Robust Representations: A Survey.

Bibliographic details on Deep Graph Structure Learning for Robust Representations: A Survey.

Graph Structure Learning for Robust Graph Neural Networks - arXiv

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to ...

YuanchenBei/Awesome-Graph-Structure-Learning - GitHub

(KDD 2020) Graph Structure Learning for Robust Graph Neural Networks [PDF] [Code]. (NIPS 2020) Iterative Deep Graph Learning for Graph Neural Networks ...

Deep Graph Structure Learning for Robust Representations: A Survey

Deep Graph Structure Learning for Robust Representations: A Survey: Paper and Code. Graph Neural Networks (GNNs) are widely used for analyzing ...

Graph Structure Learning for Robust Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs ... Relational inductive biases, deep learning, and graph ...

ChandlerBang/Pro-GNN: Implementation of the KDD 2020 ... - GitHub

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks" - ChandlerBang/Pro-GNN.

GNNBook@2023: Graph Neural Networks: Graph Structure Learning

Joint Graph Structure and Representation Learning; Connections to Other Problems. Future Directions. Robust Graph Structure Learning; Scalable Graph Structure ...

Towards Unsupervised Deep Graph Structure Learning

2016. Deep neural networks for learning graph representations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 30.

Graph structure learning | Papers With Code

Graph structure learning · Graph Structure Learning for Robust Graph Neural Networks · Graph-Bert: Only Attention is Needed for Learning Graph Representations.

Robust Graph Structure Learning via Multiple Statistical Tests

... graph representation learning when graph structures are ... Iterative deep graph learning for graph neural networks: Better and robust node embeddings.

(PDF) Graph Structure Learning for Robust Graph Neural Networks

PDF | Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are ...

[PDF] Graph Structure Learning for Robust Graph Neural Networks

A general framework Pro-GNN is proposed, which can jointly learn a structural graph and a robust graph neural network model from the perturbed graph guided ...

DGSLN: Differentiable graph structure learning neural network for ...

DGSLN: Differentiable graph structure learning neural network for robust graph representations. Author links open overlay panel. Xiaofeng Zou a , Kenli Li a

Robust Graph Structure Learning with Virtual Nodes Construction

graph neural networks; graph representation learning; deep learning. MSC: 68R10; 57M15; 62A09; 65D18; 65S05. 1. Introduction. Graph-structured ...

Graph structure learning for robust graph neural networks - Papertalk

theory, deep learning, robustness, adversarial robustness and security, graph learning, representation learning. 0. 0. 0. 0. 5:54. 06/12/2021. Adversarial Graph ...

Towards Unsupervised Deep Graph Structure Learning - Shirui Pan

Graph neural networks (GNNs) are a type of deep neural net- works aiming to learn low-dimensional representations for graph- structure data [22, 48]. Modern ...

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

[1] Deep graph structure learning for robust representations: A survey (arXiv 2021). [2] Learning to Drop: Robust Graph Neural Network via Topological ...