Graph structure learning

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

We propose Uogtag, an unsupervised graph structure learning framework to address these challenges. Uogtag optimizes graph topology through the selection of ...

Heterogeneous Graph Structure Learning for Graph Neural Networks

We make the first attempt towards learning an optimal heterogeneous graph structure for HGNNs and propose a novel framework HGSL.

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a chal- lenging combinatorial problem, owing to the in-.

Graph Structure Learning based on Mistakenly Predicted Edges ...

These noisy or incomplete edges can degrade the performance of trained GNNs. To tackle this problem, Graph Structure Learning (GSL), which focuses on finding a ...

Universal and Generalizable Structure Learning for Graph Neural ...

Wentao Zhao, Shanghai Jiao Tong University.

Heterogeneous Graph Structure Learning for Graph Neural Networks

Therefore, learning an optimal het- erogeneous graph for GNN is a fundamental problem. Recently, to adaptively learn graph structures for GNNs, graph structure ...

A Gentle Introduction to Graph Neural Networks - Distill.pub

Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph ...

Large Language Models for Graph Structure Learning - OpenReview

Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by ...

Graph Structure Learning-Based Compression Method for ...

A compression method based on Graph Structure Learning (GSL) for CNNs is proposed. It utilizes the graph learning to mine the correlation ...

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, ...

Graph Structure Learning For GNNs - Xuan Kan

Graph neural networks (GNNs) are a popular and powerful since they can incorporate graph structures. 2. In practice, however, real-world graphs are often noisy ...

Speedup Robust Graph Structure Learning with Low-Rank Information

Robust graph structure learning has been proposed to improve the. GNN performance in the face of adversarial attacks. In particular, the low-rank methods are ...

Structural Entropy Based Graph Structure Learning for Node ...

We propose a novel and effective GSL framework for node classification based on the structural information theory.

Graph Representation Learning - McGill School Of Computer Science

The discussions of graph-structured data and graph properties are relatively self-contained. However, the book does assume a background in machine learning and ...

Towards Unsupervised Deep Graph Structure Learning - YouTube

Social Network Analysis and Graph Algorithms: Structure Learning Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng and Shirui Pan: ...

Boosting Graph Structure Learning with Dummy Nodes

Empiri- cal results demonstrate that taking graphs with dummy nodes as input significantly boosts graph structure learning, and using their edge-to-vertex.

Rethinking Structure Learning For Graph Neural Networks

Graph Structure Learning (GSL) has been widely employed to enhance the performance of Graph Neural Networks (GNNs).

Molecular property prediction based on graph structure learning

In this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over ...

Robust Graph Structure Learning with Virtual Nodes Construction

As depicted in Figure 1, the VN-GSL is comprised of three components. The first component involves the utilization of virtual nodes to identify ...

Graph structure learning. | Download Scientific Diagram

Download scientific diagram | Graph structure learning. from publication: Multi-Task Time Series Forecasting Based on Graph Neural Networks | Accurate time ...