Spring 2021: PHYS 7332 – Network Data Science 2 (Machine Learning with Graphs), CRN 37113
Time: Tuesdays & Fridays 3:25 – 5:05 PM Eastern 
Place: Online via Zoom

Instructor: Tina EliassiRad
Course website on Canvas: https://northeastern.instructure.com/courses/65015

Office hours: Tuesdays 5:05 – 6:00 PM Eastern via Zoom

This 4credit graduatelevel course covers stateoftheart research on mining and learning with graphs. Topics include, but are not limited to, vertex classification, graph clustering, link prediction and analysis, graph distances, graph embedding and network representation learning, deep learning on graphs, anomaly detection on graphs, graph summarization, network inference, adversarial learning on networks, and notions of fairness in social networks.
Students are expected to have taken courses on or have knowledge of the following:
This course does not have a designated textbook. The readings are assigned in the syllabus (see below).
Here are some textbooks (all optional) on machine learning and data mining:
Date 
Lecturer 
Readings 
Tue 
Tina EliassiRad 
Ekta Gujral, Evangelos E. Papalexakis: SMACD: Semisupervised MultiAspect Community Detection. SDM 2018: 702710 Ekta Gujral, Ravdeep Pasricha, Evangelos E. Papalexakis: Beyond Rank1: Discovering Rich Community Structure in MultiAspect Graphs. WWW 2020: 452462 
Fri 
Prof. Vagelis Papalexakis 

Tue 
Team 1 
Jan Overgoor, Austin R. Benson, Johan Ugander: Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice. WWW 2019: 14091420 Jan Overgoor, George Pakapol Supaniratisai, Johan Ugander: Scaling Choice Models of Relational Social Data. KDD 2020: 19901998 
Fri 
Prof.
Johan Ugander 

Tue 
Team 2 
Peter Morales, Rajmonda Sulo Caceres, Tina EliassiRad: Selective Network
Discovery via Deep Reinforcement Learning on Embedded Spaces. Appl. Netw. Sci. (2021), forthcoming. 
Fri 
Dr. Rajmonda Caceres 

Tue 
Team 3 
José Bento, Stratis Ioannidis: A Family of Tractable Graph Metrics. Appl. Netw. Sci. 4(1): 107:1107:27 (2019) Armin Moharrer, Jasmin Gao, Shikun Wang, José Bento, Stratis Ioannidis: Massively Distributed Graph Distances. IEEE Trans. Signal Inf. Process. over Networks 6: 667683 (2020) 
Fri 
Prof. Stratis Ioannidis 

Tue 
Team 4 
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra: Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. NeurIPS 2020 Jiong Zhu, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra: Graph Neural Networks with Heterophily. AAAI 2021 
Fri 
Prof.
Danai Koutra 

Tue 
Team 5 
Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah: A Unified View on Graph Neural Networks as Graph Signal Denoising. CoRR abs/2010.01777 (2020) Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang: Graph Convolutional Networks with EigenPooling. KDD 2019: 723731 
Fri 
Prof. Jiliang Tang 

Tue 
Team 1 
Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik: Subgraph Neural Networks. NeurIPS 2020 Kexin Huang, Marinka Zitnik: Graph Meta Learning via Local Subgraphs. NeurIPS 2020 
Fri 
Prof. Marinka Zitnik 

Tue 
Team 2 
Junteng Jia, Austin R. Benson: Residual Correlation in Graph Neural Network Regression. KDD 2020: 588598 Qian Huang, Horace He, Abhay Singh, SerNam Lim, Austin R. Benson: Combining Label Propagation and Simple Models Outperforms Graph Neural Networks. CoRR abs/2010.13993 (2020) 
Fri 
Prof.
Austin Benson 

Tue 
Team 3 
Antonis Matakos, Evimaria Terzi, Panayiotis Tsaparas: Measuring and Moderating Opinion Polarization in Social Networks. Data Min. Knowl. Discov. 31(5): 14801505 (2017) 
Fri 
Prof.
Evimaria Terzi 

Tue 
Team 4 
Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong: InFoRM: Individual Fairness on Graph Mining. KDD 2020: 379389 
Fri 
Prof. Hanghang Tong 

Tue 
Teams 5 & 1 
Petar Velickovic, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell: Pointer Graph Networks. NeurIPS 2020 Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Velickovic: Principal Neighbourhood Aggregation for Graph Nets. NeurIPS 2020 [optional] Andreea Deac, Petar Velickovic, Ognjen Milinkovic, PierreLuc Bacon, Jian Tang, Mladen Nikolic: XLVIN: eXecuted Latent Value Iteration Nets. CoRR abs/2010.13146 (2020) 
Fri 
(DeepMind) 

Tue 
Team 2 
Lingxiao Zhao, Leman Akoglu: On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights. CoRR abs/2012.12931 (2021) 
Fri 
Prof.
Leman Akoglu 

Tue 
Teams 3 & 4 
Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann: Predict then Propagate: Graph Neural Networks meet Personalized PageRank. ICLR 2019 Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann: Scaling Graph Neural Networks with Approximate PageRank. KDD 2020: 24642473 
Fri 
Prof. Stephan Günnemann 

Tue 
Team 5 
Prathyush Sambaturu, Bijaya Adhikari, B. Aditya Prakash, Srinivasan Venkatramanan, Anil Vullikanti: Designing Effective and Practical Interventions to Contain Epidemics. AAMAS 2020: 11871195 (Best Paper Nominee) Vedant Das Swain, Jiajia Xie, Maanit Madan, Sonia Sargolzaei, James Cai, Munmun De Choudhury, Gregory D. Abowd, Lauren N. Steimle, B. Aditya Prakash: WiFi Mobility Models for COVID19 Enable Less Burdensome and More Localized Interventions for University Campuses. medRxiv 2021.03.16.21253662, doi: https://doi.org/10.1101/2021.03.16.21253662 (March 24, 2021) 
Fri 
Prof.
B. Aditya Prakash 