Machine Learning on Graph: Graph Signal Processing & Graph Substructure Learning

Speakers:
Maggie Cheng, Jia He

Abstract:
Machine learning on graphs is an interesting and challenging task with broad applications. Many applications require incorporating information about the link structure of the graph as well as the features on the nodes and edges into the machine learning model. In this talk we distinguish two types of graph learning problems: graph signal processing and graph substructure learning. The first focuses on learning the signals supported on graphs, i.e., the graph-structured data. We present two graph convolutional neural network models that are used to infer power line outage from measurements. The second focuses on learning graph structures and substructures. We present the well-known neural network models and our recent work on this topic, and compare their expressive power.

Bio:
Maggie Cheng is a professor of Applied Mathematics, and the director of the Center for Interdisciplinary Scientific Computation (CISC) at Illinois Institute of Technology. Her research interest is in time-evolving complex networks analysis using data-driven approaches, such as statistical anomaly detection and network inference.

Jia He is a Ph.D. student of Applied Mathematics at Illinois Institute of Technology. Her research interest is in developing neural network models for learning graph substructures and graph-structured data, and analyzing the expressive power of graph neural networks.

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