Graph Self-Supervised Learning: A Survey

A Brief Survey on Semi-supervised Learning with Graph Regularization

In this survey, we go over a few historical literatures on semi-supervised learning problems which apply graph regularization on both labled and unlabeled data to improve classification performance. These semi-supervised methods usually construct a nearest neighbour graph on instance space under certain measure function, and then work under the smoothness assumption that class labels of samples...

Supervised Learning of Graph Structure

Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present...

Graph-Based Semi-Supervised Learning

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in ...

A Survey on Inductive Semi-supervised Learning

Supervised Machine Learning Approaches: a Survey

One of the core objectives of machine learning is to instruct computers to use data or past experience to solve a given problem. A good number of successful applications of machine learning exist already, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavi...