Advancement in graph data mining: applications in unsupervised, continual, and few-shot learning - Rakaraddi, Appan (Nanyang Technological University, 2024)
Graph mining has proven to be extremely useful in analysing features and properties of real-world graphs. This enables a number of tasks including the prediction and evaluation of how information varies with changes in the link structure, generating and building models to extract properties such as link prediction, node classification and recommendation, cluster and community detection, etc. Deep learning techniques have become the prevalent approach for graph mining as they are able to take advantage of the increasing availability of graph data. Traditional deep learning models pre-process the graph data by mapping the nodes to a real vector, which can desecrate important node relationships in the graphs. Training on such pre-processed structures with the traditional deep learning models may present extremely unstable and inaccurate results. To overcome these issues, Graph Neural Network (GNN) was introduced to handle the non-Euclidean structure of the graphs for many of the classifier and regressor tasks. However, state-of-the-art GNNs require a large amount of training data, which incur an enormous burden of labelled data annotations for supervised learning. Also, GNNs vastly suffer from degradation with the increasing number of depth layers leading to oversmoothing during neighbourhood feature aggregation. The aim of this research is to overcome the above-mentioned challenges faced by GNNs for graph mining.