Representation learning for the graph data : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

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Graph data consist of the association information between complex entities and also contain diverse vertex information. To make graph data analysis simple and effective, as the bridge between the original graph data and the graph application tasks, graph representation learning has become a hot research topic in recent years. Previous representation learning methods for the graph data may not reflect the intrinsic relationship between nodes due to the complexity of the graph data. Moreover, they do not preserve the topology of the graph data well, which will affect the effectiveness of the downstream tasks. To deal with these issues, the thesis studies effective graph representation learning methods in terms of graph construction and representation learning. We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the Pearson correlation coefficient to obtain a fully connected Functional Connectivity brain Networks (FCN), and then to learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. We propose an end-to-end deep graph learning method under semi-supervised learning to improve the quality of initial graph. Specifically, the proposed method first extracts the common information and the complementary information among multiple local graphs to obtain a unified local graph, which is then fused with the global graph of the data to obtain the initial graph for the GCN model. As a result, the proposed method conducts the graph fusion process twice to simultaneously learn the low-dimensional space and the intrinsic graph structure of the data in a unified framework. We propose a multi-view unsupervised graph learning method. Specifically, the adaptive data augmentation first builds a feature graph from the feature space, and then designs a deep graph learning model on the original representation and the topology graph, respectively, to update the feature graph and the new representation. As a result, the adaptive data augmentation outputs multi-view information, which is fed into two GCNs to generate multi-view embedding features. Two kinds of contrastive losses are further designed on multi-view embedding features to explore the complementary information among the topology and feature graphs. Additionally, adaptive data augmentation and contrastive learning are embedded in a unified framework to form an end-to-end model. All proposed methods are evaluated on real-world data sets. Experimental results demonstrate that our methods outperformed all comparison methods, compared to state-of-the-art methods.
Machine learning, Neural networks (Computer science), Graph theory, Data processing