- Speaker: Dr. Liang Zhao, Emory University
- Title： Graph Neural Networks for Spatial and Temporal Networks
- Date：9:00pm US East time, 12/31/2022
- Date：10:00am Beijing time, 01/01/2023
- Zoom ID：933 1613 9423
- Zoom PWD：416262
Title: Graph Neural Networks for Spatial and Temporal Networks
Deep learning techniques have achieved tremendous success in continuous data like image and audio. They then extended such success into other data such as network- structured data which are ubiquitous in many scientific such as molecules and societal domains such as Internet of Things. Networks are commonly embedded in the space and evolve over time, resulting in spatial and temporal networks. Analyzing and modeling the interaction between spatial properties and network properties are core issue in spatial networks, which is crucial for applications such as molecule design and brain network analyses. On the other hand, Analyzing the time-variant and time-invariant aspects of networks are critical research issues in temporal networks, which benefit wide range of applications such as mobility network simulation. Historically, their modeling and analyses typically rely on the network generation principles predefined by human heuristics and prior knowledge. Such methods usually fit well towards the properties that the predefined principles are tailored for, but usually cannot do well for the others. However, in many application domains like the aforementioned ones, the network properties and generation principles are largely unknown. The recent research frontier on graph neural network provides a data-driven alternative for network representation learning and analysis. In this talk, I will first give a background of graph neural networks and then introduce our recent works on spatial and temporal networks.
Bio: Dr. Liang Zhao is an assistant professor at the Department of Compute Science at Emory University. Before that, he was an assistant professor in the Department of Information Science and Technology and the Department of Computer Science at George Mason University. He obtained his Ph.D. degree as Outstanding PhD student in 2016 from Computer Science Department at Virginia Tech in the United States. His research interests include data mining and machine learning, with special interests in spatiotemporal and network data mining, deep learning on graphs, nonconvex optimization, and interpretable machine learning. He has published over a hundred papers in top-tier conferences and journals such as KDD, TKDE, ICDM, ICLR, NeurIPS, Proceedings of the IEEE, TKDD, CSUR, IJCAI, AAAI, and WWW. He won NSF Career Award in 2020 and Jeffress Trust Award in 2019. He also won Amazon Research Award in 2020, Meta Research Award, and CRA Computing Innovation Mentor in 2021. He was ranked as “Top 20 Rising Star in Data Mining” by Microsoft Search in 2016. He won several the Best Paper Award and Candidates such as Best Paper Award in ICDM 2019, Best Paper Candidate in ICDM 2021, Best Paper Award Shortlist in WWW 2021, and Best paper Candidate in ACM SIGSPATIAL 2022. He is an IEEE senior member.