DEEP LEARNING ON GRAPHS

Abstract

In this dissertation, deep learning on graph is proposed theoretically and experimentally as a new research approach that applies machine learning, deep learning in particular, on complex and dynamic relational data that can be expressed properly by a graph. Social network services that are closely related to our lives, such as Facebook, and organisms that comprise numerous proteins with various structures, indicate the importance of graph representation and its analysis, including not only individual entity information but also comprehensive structural information among entity relationships. Unlike traditional data representation and its analysis, graph-based representation and analysis that include intrinsic geometric information will be able to facilitate further analysis through an interdisciplinary approach with deep learning, which has recently achieved remarkable results in various fields. This new approach also provides research diversity, assisting in the ultimate goal of achieving true artificial intelligence. Along these lines, proposed new approaches of machine learning to extract data-driven features more effectively and to discover new facts from complex and dynamic relational data. Throughout this dissertation, four approaches to apply machine learning are presented, specifically deep learning to graphs, spatial learning, spatial-temporal learning, and efficient learning and sampling. These approaches are described in detail in their corresponding chapters. First, a spatial learning approach is proposed to quantitatively extract the structural features of a graph and measure the similarity between graphs. The spatial learning approach is extended to a spatial-temporal learning approach that learns and predicts not only the data-driven structural features but also the dynamically changing features of graphs through deep learning. In order to improve the graph learning approaches described above, an efficiency learning approach attempts to advance deep learning for graphs by incorporating a transfer learning and sampling approach that identifies the possibility of efficient learning for large scale data having graph representation. The proposed deep learning on graph provides a comprehensive data analysis tool that is differentiated from existing data representation and analysis methods, and is validated through experimental results. It also embraces diversity of research by expanding the leverage of deep learning, which has produced remarkable results in various fields such as image, speech, and text, and is potentially of value in other unexplored domains.