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Data augmentation for deep graph learning: A survey
Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …
demonstrated remarkable performance on numerous graph learning tasks. To address the …
How to find your friendly neighborhood: Graph attention design with self-supervision
Attention mechanism in graph neural networks is designed to assign larger weights to
important neighbor nodes for better representation. However, what graph attention learns is …
important neighbor nodes for better representation. However, what graph attention learns is …
Fuzzy-based deep attributed graph clustering
Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying
underlying network structures. Recently, a variety of graph representation learning models …
underlying network structures. Recently, a variety of graph representation learning models …
A survey of deep graph clustering: Taxonomy, challenge, application, and open resource
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …
fundamental yet challenging task. Benefiting from the powerful representation capability of …
Prototypical graph contrastive learning
Graph-level representations are critical in various real-world applications, such as predicting
the properties of molecules. However, in practice, precise graph annotations are generally …
the properties of molecules. However, in practice, precise graph annotations are generally …
Rethinking graph auto-encoder models for attributed graph clustering
Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to
perform joint clustering and embedding learning. However, two critical issues have been …
perform joint clustering and embedding learning. However, two critical issues have been …
Data augmentation on graphs: a technical survey
In recent years, graph representation learning has achieved remarkable success while
suffering from low-quality data problems. As a mature technology to improve data quality in …
suffering from low-quality data problems. As a mature technology to improve data quality in …
Label-guided graph contrastive learning for semi-supervised node classification
M Peng, X Juan, Z Li - Expert Systems with Applications, 2024 - Elsevier
Semi-supervised node classification is a task of predicting the labels of unlabeled nodes
using limited labeled nodes and numerous unlabeled nodes. Recently, Graph Neural …
using limited labeled nodes and numerous unlabeled nodes. Recently, Graph Neural …
[PDF][PDF] CONGREGATE: Contrastive Graph Clustering in Curvature Spaces.
Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …
with the deep learning methods in recent years. Nevertheless, we observe that several …
Fine-grained attributed graph clustering
Graph clustering is a prevalent issue associated with social networks, data mining, and
machine learning; its objective is to detect communities or groups in networks. Inspired by …
machine learning; its objective is to detect communities or groups in networks. Inspired by …