12 research outputs found
Learning Robust Node Representations on Graphs
Graph neural networks (GNN), as a popular methodology for node representation
learning on graphs, currently mainly focus on preserving the smoothness and
identifiability of node representations. A robust node representation on graphs
should further hold the stability property which means a node representation is
resistant to slight perturbations on the input. In this paper, we introduce the
stability of node representations in addition to the smoothness and
identifiability, and develop a novel method called contrastive graph neural
networks (CGNN) that learns robust node representations in an unsupervised
manner. Specifically, CGNN maintains the stability and identifiability by a
contrastive learning objective, while preserving the smoothness with existing
GNN models. Furthermore, the proposed method is a generic framework that can be
equipped with many other backbone models (e.g. GCN, GraphSage and GAT).
Extensive experiments on four benchmarks under both transductive and inductive
learning setups demonstrate the effectiveness of our method in comparison with
recent supervised and unsupervised models.Comment: 16 page
CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW
Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serve in an occupation that is identified by their label. A complete audit without a supervisor is fairly easy to do, but nevertheless represents a significant risk to the enterprise, as there have been few chances to safely experiment with it so far. By utilizing a large number of unsupervised inputs along with the supervised inputs, the semi-supervised learning solves this issue, to create a good training sample. Since semi-supervised learning requires fewer human effort and allows greater precision, both theoretically or in practice, it is of critical interest