Topic: Fairness-aware Learning over Graphs
Speaker: Prof. Yanning Shen, Assistant Professor, University of California Irvine Samueli School of Engineering, Electrical Engineering and Computer Science
Time: 2022.11.15 (Tue) 14:00-16:00
Venue: CGU Artificial Intelligence Research Center (Management Building 11F)
Join Online: https://shorturl.at/ejuzY
About the Speaker:
Yanning Shen is an Assistant Professor with the EECS department at the University of California, Irvine. She received her Ph.D. degree from the University of Minnesota (UMN) in 2019. She was a finalist for the Best Student Paper Award at 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 a Rising Star in EECS by Stanford University in 2017. She received the Microsoft Academic Grant Award for AI Research in 2021, the Google Research Scholar Award in 2022, and the Hellman Fellowship in 2022. Her research interests span the areas of machine learning, network science, data science, and signal processing. More detailed information can be found at: https://sites.google.com/uci.edu/yanning-shen
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.
Organizers: Artificial Intelligence Research Center & College of Intelligent Computing
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