Temporal learning for dynamic graph

A graph is a data structure to model a complex system of entities connected in a particular relation.These entities are nodes in the graph, and the connections are edges.A dynamic graph is a graph that evolves in its nodes, edges or both.We can use it to analyze networks that are not static, such as social networks, academic citation networks and city traffic networks.The dynamic graph is a widely used data structure in various domains.However, the exploration of machine learning with dynamic graphs is still in its early stage.What are the drivers of a dynamic graph's evolution?How can we learn the temporal information from a dynamic graph's history?How can we determine if we should use dynamic graph learning algorithms to analyze a given graph?These are still open questions that are well worth to explore.In this thesis, I try to address the research questions above by a survey of recently developed supervised dynamic graph learning algorithms and proposing a dynamic graph temporal learning framework.Based on the framework above, I conducted an initial study on measuring the significance of temporal patterns to prepare for the research in predicting performance gain from dynamic graph learning algorithms.During my study at McGill, I received in-depth training on various topics about Artificial Intelligence (AI) and Machine Learning(ML), especially Natural Language Processing, Network Science and the foundation theory of AI and ML.I appreciate our faculty's effort in developing such a fantastic program syllabus.When I worked with my supervisor Dr.Xue Liu, I learned from him how to do research from a high-level point of view, how to discover and focus on the high-impact research ideas, how to plan our work more efficiently, and many other best practices in conducting scientific research.All of these are very beneficial to my future career.The collaboration system he established between his students and other research groups also opened up my eyesight to different scientific domains.I am genuinely thankful to my supervisor Dr.Xue Liu.If there is one thing I regret during my study at McGill, that would be my giving up on the research idea that Dr.Kieran O'Donnell helped me establish.I selected to work on a start-up idea instead of continuing that potential high-impact work.I must say sorry to Dr.Kieran O'Donnell for not continuing to work with him because of my greediness.I also need to say 'thank you' to

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