Graphs are effective data structures for modeling relationships in data. As interest in graphs increases with the development of graph neural networks, various works have been conducted to study fundamental problems of machine learning in the graph domain. This talk introduces our recent works for positive-unlabeled (PU) learning and data augmentation on graphs. The first part introduces our work (ICDM-21) for solving PU learning on graphs by modeling real-world graphs as Markov networks with an EM formulation. The second part introduces our work (WWW-22) for designing model-agnostic algorithms that augment the structure of real-world graphs while minimizing changes in semantic information.
Jaemin Yoo is a researcher in the fields of data mining and machine learning, and will work as a postdoctoral researcher at Carnegie Mellon University from March. His primary research interest is graph mining, while he also studied various topics in data mining such as interpretable tree models or time series analysis. His work has been presented at top conferences such as WWW, KDD, ICDM, WSDM, or NeurIPS. He is a recipient of the Google PhD Fellowship and Qualcomm Innovation Fellowship. He received his Ph.D. and B.S. in Computer Science and Engineering from Seoul National University.