Data-Efficient Graph Learning

Speaker:
Mr. DING Kaize

Abstract:

The world around us — and our understanding of it — is rich in relational structure: from atoms and their interactions to objects and entities in our environments. Graphs, with nodes representing entities and edges representing relationships between entities, serve as a common language to model complex, relational, and heterogeneous systems. Despite the success of recent deep graph learning, the efficacy of existing efforts heavily depends on the ideal data quality of the observed graphs and the sufficiency of the supervision signals provided by the human-annotated labels, leading to the fact that those carefully designed models easily fail in resource-constrained scenarios.

In this talk, I will present my recent research contributions centered around data-efficient learning for relational and heterogeneous graph-structured data. First, I will introduce what data-efficient graph learning is and my contributions to different research problems under its umbrella, including graph few-shot learning, graph weakly-supervised learning, and graph self-supervised learning. Based on my work, I will elucidate how to push forward the performance boundary of graph learning models especially graph neural networks with minimal human supervision signals. I will also touch upon the applications of data-efficient graph learning to different domains and finally conclude my talk with a brief overview of my future research agenda.

Biography:

DING Kaize is currently a Ph.D. candidate from the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). Kaize is working at the Data Mining and Machine Learning (DMML) Lab with Prof. Huan Liu and previously he was previously interned at Google Brain, Microsoft Research, and Amazon Alexa AI. Kaize is broadly interested in the areas of data mining, machine learning, and natural language processing and their interdisciplinary applications in different domains including cybersecurity, social good, and healthcare. His recent research interests particularly focus on data-efficient learning and graph neural networks. He has published a series of papers in top conferences and journals such as AAAI, EMNLP, IJCAI, KDD, NeurIPS, and TheWebConf. Kaize was the recipient of the ASU Graduate College Completion Fellowship and ASU GPSA Outstanding Research Award, etc. More information about him can be found at https://www.public.asu.edu/~kding9/ .

Join Zoom Meeting:
https://cuhk.zoom.us/j/99778568306?pwd=Nms0cm9takVNQWtRaDhuaVdaTVJ5dz09

Enquiries: Mr Jeff Liu at Tel. 3943 0624