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
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Instructor: Tina Eliassi-Rad
Course website on Canvas: https://northeastern.instructure.com/courses/65015
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Office hours: Tuesdays 5:05 – 6:00 PM Eastern via Zoom
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This 4-credit graduate-level course covers state-of-the-art 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 Eliassi-Rad |
Ekta Gujral, Evangelos E. Papalexakis: SMACD: Semi-supervised Multi-Aspect Community Detection. SDM 2018: 702-710 Ekta Gujral, Ravdeep Pasricha, Evangelos E. Papalexakis: Beyond Rank-1: Discovering Rich Community Structure in Multi-Aspect Graphs. WWW 2020: 452-462 |
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: 1409-1420 Jan Overgoor, George Pakapol Supaniratisai, Johan Ugander: Scaling Choice Models of Relational Social Data. KDD 2020: 1990-1998 |
Fri |
Prof.
Johan Ugander |
|
Tue |
Team 2 |
Peter Morales, Rajmonda Sulo Caceres, Tina Eliassi-Rad: Selective Network Discovery via
Deep Reinforcement Learning on Embedded Spaces. CoRR abs/1909.07294v2
(2020) |
Fri |
Dr. Rajmonda
Caceres |
|
Tue |
Team 3 |
José Bento, Stratis Ioannidis: A Family of Tractable Graph Metrics. Appl. Netw. Sci. 4(1): 107:1-107:27 (2019) Armin Moharrer, Jasmin Gao, Shikun Wang, José Bento, Stratis Ioannidis: Massively Distributed Graph Distances. IEEE Trans. Signal Inf. Process. over Networks 6: 667-683 (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. CoRR abs/2009.13566 (2020) |
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: 723-731 |
Fri |
Prof.
Jiliang Tang |
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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: 588-598 Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin R. Benson: Combining Label Propagation and Simple Models Out-performs 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): 1480-1505 (2017) |
Fri |
Prof.
Evimaria Terzi |
|
Tue |
Team 4 |
Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong: InFoRM: Individual Fairness on Graph Mining. KDD 2020: 379-389 |
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, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic: XLVIN: eXecuted Latent Value Iteration Nets. CoRR abs/2010.13146 (2020) |
Fri |
(DeepMind) |
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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 (2020) |
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: 2464-2473. |
Fri |
Prof. Stephan
Günnemann |
|
Tue |
Team 5 |
To be determined |
Fri |
Prof.
B. Aditya Prakash |