Universal and Generalizable Structure Learning for Graph Neural ...

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Hence, motivated by the remarkable success of deep neural networks, which are arguably universal approximators, in this work we develop a graph-based deep ...

Qitian Wu - DBLP

GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks. ... GraphGLOW: Universal and Generalizable Structure Learning for Graph ...

Graph Structure Learning for Robust Graph Neural Networks

Request PDF | On Aug 23, 2020, Wei Jin and others published Graph Structure Learning for Robust Graph Neural Networks | Find, read and cite all the research ...

GNNBook@2023: Graph Neural Networks: Graph Structure Learning

Abstract. Due to the excellent expressive power of Graph Neural Networks (GNNs) on modeling graph-structure data, GNNs have achieved great success in ...

Universal Ensemble‐Embedding Graph Neural Network for Direct ...

Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures ...

Heterogeneous Graph Structure Learning for Graph Neural Networks

Adaptive Universal Generalized PageRank Graph Neural Network · Eli Chien, Jianhao Peng, Pan Li, Olgica Milenkovic. Keywords Abstract Paper · Over-smoothing ...

Improving fraud detection via imbalanced graph structure learning

Adaptive universal generalized pagerank graph neural network. In International conference on learning representations. Corizzo, R., & Slenn, T. (2022) ...

Graph Contrastive Learning with Augmentations - NIPS

Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs).

Unsupervised Graph Structure Learning Based on Optimal ... - MDPI

Graph neural networks (GNNs) are effective for structured data analysis but face reduced learning accuracy due to noisy connections and the necessity for ...

Understanding Graph Neural Networks with Generalized Geometric ...

... learning tasks, they typically assume that inputted data have a Euclidean gridlike structure. However, many data sets of interest, such as social networks ...

Learning finite element convergence with the Multi-fidelity Graph ...

To further extend the generalizability of machine learning models, Graph Neural Networks (GNNs) use neural networks to learn relations between data in graph ...

New Frontiers in Graph Learning - NeurIPS 2024

-. Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks ( Poster ) > link · Link. Samuel Leventhal · Attila Gyulassy · ...

Haggai Maron

From Local Structures to Size Generalization in Graph Neural Networks ... Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks.

Discovering Robust and Generalizable Graph Lottery Tickets

The training and inference of Graph Neural Networks (GNNs) are ... graphs by jointly pruning the graph structure and the model weights.

Federated Graph Semantic and Structural Learning - IJCAI

Second, we postulate that a well-structural graph neural net- work possesses ... generate a generalizable model [Xie et al., 2021]. In contrast, intra ...

Graph Representation Learning - McGill School Of Computer Science

generalizations of convolutional neural networks to graph-structured data, and neural ... Graphs are a ubiquitous data structure and a universal language ...

Multi-Domain Generalized Graph Meta Learning

Graph Neural Networks: Recently Graph Neural Net- works (GNNs) have become an effective way to learn graph structure with deep neural networks. With the ...

Pre-training Interpretable Graph Neural Networks - Microsoft Research

... universal structural patterns of different graphs is until-now unexplored. Motivated by the great success of recent pre-training techniques ...

Adaptive Universal Generalized PageRank Graph Neural Network

... learning ability on general graph-structured data. First, most of them seem to be tailor-made to work on homophilic (associative) graphs. The homophily ...

Universal and interpretable classification of atomistic structural ...

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning ... universal graph neural ...