Towards Trustworthy Graph Learning

Speaker: Feng Xia (Federation University Australia)

Abstract

Graphs (or networks) are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs or graph machine learning) is gaining huge attention from both researchers and practitioners. Graph learning proves effective for many tasks in real-world applications, such as regression, classification, clustering, matching, and ranking. Over the past few years, a lot of graph learning models and algorithms (e.g., graph neural networks, network embedding, network representation learning, etc.) have been developed. Nevertheless, the field of graph learning is facing many challenges deriving from, e.g., fundamental theory and models, algorithms and methods, supporting tools and platforms, and real-world deployment and engineering. This talk will give an overview of the state of the art of trustworthy graph learning, paying special attention to relevant trends and challenges. Some recent advancements in this field will be showcased.