CS7301 Advanced machine learning in complex networks

Instructor Feng Chen
Office ECSS 3.901
Number (518) 442-4270
Email feng.chen@utdallas.edu
Office Hour
Tuesdays 1:00PM to 2:00PM; Fridays 8:45AM to 9:45AM

 

TA Yi-Hui Lee
Office
Number  
Email Yi-Hui.Lee@UTDallas.edu
Office Hour

 

Class Time and Location Fr 10:00AM-12:45PM, JO 3.516

 

Course Description:

This seminar course introduces advanced machine learning and deep learning techniques for detecting and forecasting patterns in complex networks. Examples of applications include disease outbreak detection using public health data, such as hospital visits and medication sales; detection and prediction of cyber attacks (e.g., spammers, fake users, and compromised normal users) using from social networks and financial data, discovery of anomalous or novel patterns from knowledge graph data, and crowdsourcing human mobility and social media data to detect traffic congestion, air pollution, and power leakage.

In this course, I will lead the students in reading and discussion of a collection of machine learning and data mining papers related to graph mining and deep learning (e.g., deep reinforcement learning, graph neural networks, few-short learning, and meta-learning) that I have carefully selected. My goal is to help the students fully understand the papers and learn advanced machine learning techniques that will be helpful to their research.

Starting from Mar. 30th, we will go online. A web conferencing tool called Blackboard Collaborate has been made available in your eLearning section. The tool is located in the left-hand side course menu.

Course Description:

Topic Paper # Paper Title Reading Materials Presenters
Graph Neural Networks 1 Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." ICLR, 2019. (2407 citations) Jincheng Li
Graph Neural Networks 2 Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.. (829 citations) Haoliang Wang
Graph Neural Networks 3 Knyazev, Boris, Graham W. Taylor, and Mohamed Amer. "Understanding Attention and Generalization in Graph Neural Networks." Advances in Neural Information Processing Systems. 2019. Saloni Agarwal, Diksha Godbole
Meta Learning 4 Finn, Chelsea, Pieter Abbeel, and Sergey Levine. "Model-agnostic meta-learning for fast adaptation of deep networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017. (1248 citations) Junfeng Guo, Yibo Hu
Few-Shot Learning, Graph Neural Networks 5 Victor Garcia Satorras, Joan Bruna Estrach: Few-Shot Learning with Graph Neural Networks. ICLR (Poster) 2018 Harsha Kokel, Zelun Kong
Meta Learning, Graph Neural Networks, Adversarial Attacks 6 Adversarial Attacks on Graph Neural Networks via Meta Learning Shuo Li, Changbin Li
Meta Learning, Causual Learning 7 A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yuzhe Ou, Omer Ozarslan
Meta Learning, 8 Shahab Shams, Guihong Wan
9 Zhuoyi Wang, Chengen Wang
10
11 Wu Hao, Kairui Xu
12 Qifan Zhang, Zhao Chen
13 Xujiang Zhao

Reading Materials:

Tutorial on Fourier Transformation and Wavelet Transformation: Part I Part II Part III Part IV My Notes

Papers related to spectrum-domain graph neural networks

Examinations and Grading:

Course Project Requirement