Hanghang Tong

About

The main focus of IDEA Lab@UIUC lies in large scale data mining, machine learning and AI, especially for graph and multimedia data with applications to social networks analysis, healthcare, cyber-security, cyber-physical systems, agriculture and e-commerce.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data Mining
• Theoretical computer science
• Econometrics
• Operations management
• Physics
• Economics

Selected publications

• Data Augmentation for Deep Graph Learning

  ACM SIGKDD Explorations Newsletter · 2022 · 198 citations

  Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. However, conventional data augmentation methods can hardly handle graph-structured data which is defined in non-Euclidean space with multi-modality. In this survey, we formally formulate the problem of…

  DOI

• Few-shot Network Anomaly Detection via Cross-network Meta-learning

  2021 · 119 citations

  Network anomaly detection, also known as graph anomaly detection, aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a variety of applications ranging from finance, healthcare to social network analysis. Due to the unbearable labeling cost, existing methods are predominately developed in an unsupervised manner. Nonetheless, the anomalies they identify may turn out to be data noises or uninterest…

  DOI

• InFoRM: Individual Fairness on Graph Mining

  2020 · 99 citations

  Senior authorCorresponding

  Algorithmic bias and fairness in the context of graph mining have largely remained nascent. The sparse literature on fair graph mining has almost exclusively focused on group-based fairness notation. However, the notion of individual fairness, which promises the fairness notion at a much finer granularity, has not been well studied. This paper presents the first principled study of Individual Fairness on gRaph Mining (InFoRM). First, we present a generic definition of individual fairness for gra…

  DOI

• Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective

  ACM SIGKDD Explorations Newsletter · 2025-07-07 · 11 citations

  articleSenior author

  Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new information. Traditional symbolic reasoning, despite its strengths, struggles with the challenges posed by incomplete and noisy data within these graphs. In contrast, the rise of Neural Symbolic AI marks a significant advancement, merging the robustnes…

  PublisherDOI

• Harnessing Vision Models for Time Series Analysis: A Survey

  2025-09-01 · 4 citations

  article

  Time series analysis has evolved from traditional autoregressive models to deep learning, Transformers, and Large Language Models (LLMs). While vision models have also been explored along the way, their contributions are less recognized due to the predominance of sequence modeling. However, challenges such as the mismatch between continuous time series and LLMs’ discrete token space, and the difficulty in capturing multivariate correlations, have led to growing interest in Large Vision Models (L…

  PublisherDOI

Recent grants

• EAGER: Collaborative Research: Correspondence Discovery in Disparate Networks

  NSF · $50k · 2017–2018

• FAI: Towards a Computational Foundation for Fair Network Learning

  NSF · $602k · 2020–2024

• CAREER: Network Robustification: Theories, Algorithms and Applications

  NSF · $483k · 2019–2025

Frequent coauthors

• Jingrui He

  56 shared

• Jian Pei

  Duke University

  51 shared

• Yuan Yao

  48 shared

• Christos Faloutsos

  Carnegie Mellon University

  45 shared

• Bang Ye Wu

  37 shared

• Ee‐Peng Lim

  Singapore Management University

  37 shared

• Shirui Pan

  37 shared

• Xingliang Yuan

  37 shared

Labs

• IDEA Lab@UIUCPI

  Large scale data mining, machine learning and AI, especially for graph and multimedia data with applications to social networks analysis, healthcare, cyber-security, cyber-physical systems, agriculture and e-commerce.

Education

• Ph.D.

  University of Illinois at Urbana-Champaign

• M.S.

  University of Illinois at Urbana-Champaign

• B.S.

  University of Illinois at Urbana-Champaign

Awards & honors

• ACM Fellow, 2025
• Senior Member, AAAI, 2025
• University Scholar, UIUC, 2024
• IEEE ICDM 2022 10-Year Highest Impact Paper Award, 2022
• Springer Knowl. Inf. Syst. (KAIS) on “Best-ranked paper of I…

Similar researchers at University of Illinois Urbana-Champaign

• Jiawei Hanh-146
• Kaiyu Guanh-86
• ChengXiang Zhaih-83