Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today’s world we are generating and gathering data in a much faster and diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way.
MLoG at ICDM'22 provides a venue to gather the academia researchers and industry researchers/practitioners to present the recent progress of machine learning on graphs.
Stevens Institute of Technology
University of California Riverside
Senior Staff Research Scientist
University of Illinois Urbana-Champaign
New Jersey Institute of Technology
Senior Machine Learning Engineer
Lead Research Scientist
ARC Future Fellow and Senior Lecturer