Semi-supervised and un-supervised clustering: A review and experimental evaluation
K Taha - Information Systems, 2023 - Elsevier
Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …
efficient mechanism for overcoming these challenges is to cluster the data into a compact …
Towards data-centric graph machine learning: Review and outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …
to drive AI models and applications, has attracted increasing attention in recent years. In this …
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 …
G-mixup: Graph data augmentation for graph classification
This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …
generalization and robustness of neural networks by interpolating features and labels …
Hard sample aware network for contrastive deep graph clustering
Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …
Cluster-guided contrastive graph clustering network
Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …
learning has achieved promising performance in the field of deep graph clustering recently …
Local augmentation for graph neural networks
Abstract Graph Neural Networks (GNNs) have achieved remarkable performance on graph-
based tasks. The key idea for GNNs is to obtain informative representation through …
based tasks. The key idea for GNNs is to obtain informative representation through …
Mixup for node and graph classification
Mixup is an advanced data augmentation method for training neural network based image
classifiers, which interpolates both features and labels of a pair of images to produce …
classifiers, which interpolates both features and labels of a pair of images to produce …
An empirical study of graph contrastive learning
Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph
representations without human annotations. Although remarkable progress has been …
representations without human annotations. Although remarkable progress has been …
Graph data augmentation for graph machine learning: A survey
Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …
demonstrated ability to improve model performance and generalization by added training …