学术报告

承办单位：网络与信息系统研究所

主讲人：加州大学洛杉矶分校副教授孙怡舟博士

Time：2019-06-21  14:00-16:00

Venue：Room 2736, Science Building 2

Neural Operators for Graph-Level Proximity

Computation and Representation Learning

Speaker: Yizhou Sun, Ph.D.

Associate Professor

Department of Computer Science

University of California, Los Angeles

yzsuncs.ucla.edu

Abstract

Graph neural networks (GNNs) have received more and more attention in past several years, due

to the wide applications of graphs and networks, and the superiority of their performance

compared to traditional heuristics-driven approaches. However, most existing GNNs still focus on

node-level applications, such as node classification and link prediction, and many challenging

graph tasks are graph-level, such as graph classification and graph similarity search. In this talk,

I will introduce our recent progress on graph-level neural operator development. In particular,

we will examine two challenging tasks: (1) how can we conduct efficient graph similarity search

by turning the NP-Complete GED computation problem into a learning problem? and (2) how can

we provide a neural operator that can turn any graph into a low dimensional representation

vector, which is learnable, inductive, and unsupervised. In the end, I will briefly introduce our

work on graph pre-training, which can learn generic structural features from synthetic graphs

and apply to graphs in new domains.

Speaker’s bio:

Yizhou Sun is an associate professor at department of computer science of UCLA. Prior to that,

she was an assistant professor in the College of Computer and Information Science of

Northeastern University. She received her Ph.D. in Computer Science from the University of

Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/

networks, and more generally in data mining, machine learning, and network science, with a

focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world

applications. She is a pioneer researcher in mining heterogeneous information network, with a

recent focus on deep learning on graphs/networks. Yizhou has over 100 publications in books,

journals, and major conferences. Tutorials of her research have been given in many premier

conferences. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD

Doctoral Dissertation Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF

CAREER Award, 2016 CSILLINOIS Distinguished Educator Award, 2018 Amazon Research Award,

and 2019 Okawa Foundation Research Grant.