学术报告：Open-Set Graph Domain Adaptation via Separate Domain Alignment

报告时间：6月21日（星期五）上午10:00-12:00

报告地点：沙河校区，学院1号楼102会议室

报告人：姬朋生，乔治亚大学，副教授

报告摘要：Domain adaptation has become an attractive learning paradigm, as it can leverage source domains with rich labels to deal with classification tasks in an unlabeled target domain. A few recent studies have developed domain adaptation approaches for graph-structured data. In the case of node classification task, current domain adaptation methods only focus on the closed-set setting, where source and target domains share the same label space. A more practical assumption is that the target domain may contain new classes that are not included in the source domain. Therefore, in this paper, we introduce a novel and challenging problem for graphs, i.e., open-set domain adaptive node classification, and propose a new approach to solve it. Specifically, we develop an algorithm for efficient knowledge transfer from a labeled source graph to an unlabeled target graph under a separate domain alignment (SDA) strategy, in order to learn discriminative feature representations for the target graph. Our goal is to not only correctly classify target nodes into the known classes, but also classify unseen types of nodes into an unknown class. Experimental results on real-world datasets show that our method outperforms existing methods on graph domain adaptation.

报告人简介：姬朋生教授本科、硕士毕业于南开大学，博士毕业于康奈尔大学，目前是乔治亚大学统计系的副系主任。