Dr. Hanghang Tong is a Professor at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. His research interests span large-scale data mining, machine learning, and artificial intelligence, with a particular emphasis on graphs and networks. He is a Fellow of both IEEE and ACM, a University Scholar, and a recipient of the IEEE ICDM Tao Li Award (2019).
His work on graph proximity has made a profound and lasting impact, significantly improving computational scalability, advancing the fundamental understanding of graph proximity, and broadening its applications. Notably, his IEEE ICDM 2006 paper introduced a highly efficient algorithm that achieved up to two orders of magnitude speedups over state-of-the-art methods. This work received the IEEE ICDM 2006 Best Research Paper Award and was subsequently recognized with the IEEE ICDM 2015 10-Year Highest Impact Paper Award, underscoring its enduring influence on the data mining community.
Another major contribution is his development of the “Network of X” research direction, which establishes a new framework for leveraging networks as contextual structures to connect and jointly analyze diverse datasets and data mining models, supported by a cross-network consistency principle and scalable computation. His ACM KDD 2014 paper established this perspective and motivated a series of follow-up advances in multi-network clustering, cross-network dependence inference, network-of-time-series analysis, and network alignment. Notably, his IEEE ICDM 2013 work on scalable bipartite graph alignment received the IEEE ICDM 2022 10-Year Highest Impact Paper Award, recognizing its profound and lasting impact on the data mining community.
Professor Tong has also made significant contributions to the theoretical and algorithmic foundations of graph connectivity optimization. His IEEE ICDM 2010 paper introduced efficient methods for identifying critical nodes by exploiting spectral properties and diminishing-returns principles, with important implications for network resilience and immunization strategies. His subsequent work, including the ACM CIKM 2012 Best Paper Award-winning research and an ACM TKDD 2021 paper, further advanced the field by establishing fundamental connections among various graph connectivity measures, computational hardness, approximation guarantees, and scalable optimization algorithms.