ECE Seminar Lecture Series

Fairness-aware learning over graphs

Yanning Shen, Assistant Professor, Department of EECS, University of California, Irvine

Tuesday, October 5, 2021
12:30 p.m.–1:30 p.m.

Wegmans Hall 1400

Zoom simulcast:
Passcode: 839313



We live in an era of big data and "small world’’, where a large amount of data resides on highly connected networks representing a wide range of physical, biological, and social interdependencies, e.g., social networks, and smart grids. Learning from graph/network data is hence expected to bring significant science and engineering advances along with consequent improvements in quality of life. Node representation learning has demonstrated its effectiveness for various applications on graphs. Particularly, recent developments in graph neural networks and contrastive learning have led to promising results in node representation learning for a number of tasks such as node classification, link prediction. Despite the success of graph learning, fairness is largely under-explored in the field, which may lead to biased results towards underrepresented groups in the networks. To this end, this talk will first introduce novel fairness-aware graph augmentation designs to address fairness issues in learning over graphs. New fairness notions on graphs are introduced, which serve as guidelines for the proposed graph augmentation designs. Furthermore, theoretical analysis is provided to prove that the proposed adaptation schemes can reduce intrinsic bias. Experimental results on real networks are presented to demonstrate that the proposed framework can enhance fairness, while providing comparable accuracy to state-of-the-art alternative approaches for node classification, and link prediction tasks.


Yanning Shen is an assistant professor with EECS department at the University of California, Irvine. She received her Ph.D. degree from the University of Minnesota, Twin Cities in 2019. Her research interests include machine learning, data science, network science, optimization and statistical signal processing. She was a Best Student Paper Award finalist of the 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, and the 2017 Asilomar Conference on Signals, Systems, and Computers. She was selected as Rising Stars in EECS by Stanford University in 2017 and received the UMN Doctoral Dissertation Fellowship in 2018. She received Microsoft Academic Grants for AI Research in 2021. More detailed information can be found at: