Graph structure learning
GNNBook@2023: Graph Neural Networks: Graph Structure Learning
This chapter attempts to provide a comprehensive introduction of graph structure learning through the lens of both traditional machine learning and GNNs.
YuanchenBei/Awesome-Graph-Structure-Learning - GitHub
A curated list of papers on graph structure learning (GSL). - YuanchenBei/Awesome-Graph-Structure-Learning.
A Survey on Graph Structure Learning: Progress and Opportunities
Title:A Survey on Graph Structure Learning: Progress and Opportunities ... Abstract:Graphs are widely used to describe real-world objects and ...
In order to tackle the above challenges, graph structure learning aims to dis- cover useful graph structures from data for better graph representation learning ...
Graph structure learning | Papers With Code
59 papers with code • 1 benchmarks • 2 datasets. Semi-supervised node classification when a graph structure is not available.
GSLB: The Graph Structure Learning Benchmark
Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks ...
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and ...
Universal and Generalizable Structure Learning for Graph Neural ...
This paper explores a new direction that moves forward to learn a universal structure learning model that can generalize across graph datasets in an open world.
zepengzhang/awesome-graph-structure-learning - GitHub
awesome-graph-structure-learning. Awesome. A collection of papers on Graph Structural Learning (GSL). Will be frequently updated. We have developed a ...
Graph Neural Networks: Graph Structure Learning - SpringerLink
This chapter attempts to provide a comprehensive introduction of graph structure learning through the lens of both traditional machine learning and GNNs.
DGSLN: Differentiable graph structure learning neural network for ...
In this work, we propose a novel differentiable graph structure learning neural network (DGSLN), which learns suitable graph structures for GNNs. Specifically, ...
Towards Unsupervised Deep Graph Structure Learning
In this paper, we propose a more practical GSL paradigm, unsupervised graph structure learning, where the learned graph topology is optimized by ...
Graph structure learning layer and its graph convolution clustering ...
In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by- ...
Graph Structure Learning for Robust Graph Neural Networks
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to ...
KDD 2023 - Graph Structure Learning via Progressive Strategy
Huizhao Wang, Hikvision Research Institute Considering that each node has its own characteristics, we believe graph structure learning via ...
A Unified Framework for Structured Graph Learning via Spectral ...
In this paper, we introduce a unified framework for structured graph learning that combines Gaussian graphical model and spectral graph theory.
Graph Neural Networks: Graph Structure Learning - Semantic Scholar
This chapter attempts to provide a comprehensive introduction of graph structure learning through the lens of both traditional machine learning and GNNs.
Principal Graph and Structure Learning Based on Reversed Graph ...
We develop a novel principal graph and structure learning framework that captures the local information of the underlying graph structure based on reversed ...
Graph Structure Learning for Robust Graph Neural Networks - [2020]
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs, due to the wide applications of graphs and networks, ...
Graph structure learning based on feature and label consistency
Graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly ...