Optimal Deep Graph Learning: Towards a New Frontier

Speaker:  Hanghang Tong – Urbana, USA
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Abstract

The emergence of deep learning models designed for graph and network data, often under an umbrella term named graph neural networks (GNNs for short), has largely streamlined many graph learning problems. In the vast majority of the existing works, they aim to answer the following question, that is, given a graph, what is the best GNNs model to learn from it? In this talk, we introduce the graph sanitation problem, to answer an orthogonal question. That is, given a mining task and an initial graph, what is the best way to improve the initially provided graph? We formulate the graph sanitation problem as a bilevel optimization problem, and further instantiate it by semi-supervised node classification, together with an effective solver named Gasoline. I will also introduce other works we recently did on learning optimal graphs and share my vision for the future directions.

About this Lecture

Number of Slides:  ~50
Duration:  45 minutes
Languages Available:  English
Last Updated:  18/03/2026

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