[Seminar] Mining and Learning with Graphs: Clustering, Hypergraphs, and Representation Learning
호스트: 강 유 교수 (x7254)
Graphs are useful tools to model real-world data that are best represented by a set of objects and the relationships between the objects, e.g., WWW, social networks, and biological networks among others. This talk mainly focuses on mining and learning methods for graphs with three specific topics: clustering, hypergraphs, and representation learning. The talk begins with introducing basic concepts of graph clustering and efficient solutions to various graph clustering problems from the traditional graph partitioning problems to overlapping community detection problems. The graph clustering formulations can be seamlessly extended to hypergraphs which represent complex relationships among entities by allowing a hyperedge to include two or more entities. In many practical applications, entities have various features or attributes, and sometimes entity labels are partially available. To incorporate this information, a multi-view semi-supervised clustering method is introduced. Finally, a structure-based pre-training method of knowledge graph embedding is explained where a metagraph is learned based on the structural similarity between entities in a knowledge graph.
온라인 줌 링크: https://snu-ac-kr.zoom.us/j/3349805948
Joyce Jiyoung Whang is an assistant professor of School of Computing at KAIST, where she leads the Big Data Intelligence laboratory from July 2020. She received her PhD degree in Computer Science from the University of Texas at Austin in December 2015 under the supervision of Professor Inderjit S. Dhillon. She was an assistant professor of Computer Science and Engineering at Sungkyunkwan University (SKKU) from March 2016 to June 2020. She was also affiliated with the graduate school of Artificial Intelligence at SKKU from Fall 2019 to June 2020. She led the Big Data laboratory at SKKU and actively collaborated with industries such as Samsung Electronics, NAVER, and SK Broadband. Her main research interests include graph machine learning, data mining, big data analytics, and data intelligence. In particular, she focuses on developing novel computational algorithms for graph models that arise in the fields of data mining and machine learning.
황지영 교수는 2020년 7월부터 KAIST 전산학부에 재직 중이며, 빅데이터 지능 연구실을 이끌고 있다. 황지영 교수는 2015년 12월 미국 텍사스 오스틴 대학교(University of Texas at Austin)에서 Inderjit S. Dhillon 교수의 지도 하에 컴퓨터학(Computer Science)으로 박사 학위를 취득하였다. 박사학위 취득 후, 2016년 3월부터 2020년 6월까지 성균관대학교 소프트웨어학과와 인공지능대학원에서 조교수로 재직하며 빅데이터 연구실을 이끌었으며, 삼성전자, 네이버, SK브로드밴드 등의 회사들과 활발한 산학협력 연구를 진행하였다. 황지영 교수의 주요 관심 연구분야는 데이터 마이닝, 빅데이터 분석, 데이터 지능, 그래프 기계학습 등이다. 특히, 그래프 모델로 표현되는 데이터에 대한 새로운 기계학습 및 마이닝 알고리즘을 개발하는 연구를 주로 수행하고 있다.