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Spring 2021: PHYS 7332 – Network Data Science 2 (Machine Learning with Graphs), CRN 37113

 

General Information

Time: Tuesdays & Fridays 3:25 – 5:05 PM Eastern

Place: Online via Zoom

Instructor: Tina Eliassi-Rad

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

Office hours: Tuesdays 5:05 – 6:00 PM Eastern via Zoom
Also, available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu
to setup appointment; begin the subject line with [sp21 nets].

 

Overview

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.

 

Prerequisites

Students are expected to have taken courses on or have knowledge of the following:

o   Calculus and linear algebra

o   Basic statistics, probability, machine learning, or data mining

o   Algorithms and programming skills (e.g., Python, Julia, C, Java, Matlab, or any programming language of their preference)

 

Textbooks

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:

 

Resources

 

Grading

o   Class presentations (45%)

o   Students team up into groups of three.

o   Each week has an assigned team. That team is responsible for presenting the readings for that week.

o   Besides the readings, each paper is likely to have additional materials on the Web. Examples include supplemental materials, video, code, data, etc. These are helpful for presentations and class projects.

o   During the semester, each team will present three times.

o   Class project (55%)

o   Each team will choose (by Saturday February 20, 2021 at 11:59 PM Eastern) one of the papers in the syllabus to replicate.

o   In addition to replication, each team will propose extension(s) to the chosen paper and implement those extension(s).

o   Each team will write a report (maximum 6 pages) detailing what was learned. Use the style files at https://paperswithcode.com/static/rc2020/ML-Reproducibility-Challenge-2020-Template.zip.

o   Reports are due on Wednesday April 28, 2021 at 11:59 PM Eastern.

 

Schedule/Syllabus (Subject to Change)

Date

Lecturer

Readings

Tue
Jan 19

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
Jan 22

Prof. Vagelis Papalexakis
(University of California Riverside)

Tue
Jan 26

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
Jan 29

Prof. Johan Ugander
(Stanford University)

Tue
Feb 2

Team 2

Peter Morales, Rajmonda Sulo Caceres, Tina Eliassi-Rad: Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces. Appl. Netw. Sci. (2021), forthcoming.

Fri
Feb 5

Dr. Rajmonda Caceres
(MIT Lincoln Laboratory)

Tue
Feb 9

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
Feb 12

Prof. Stratis Ioannidis
(Northeastern University)

Tue
Feb 16

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
Feb 19

Prof. Danai Koutra
(University of Michigan)

Tue
Feb 23

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
Feb 26

Prof. Jiliang Tang
(Michigan State University)

Tue
Mar 2

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
Mar 5

Prof. Marinka Zitnik
(Harvard University)

Tue
Mar 9

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
Mar 12

Prof. Austin Benson
(Cornell University)

Tue
Mar 16

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
Mar 19

Prof. Evimaria Terzi
(Boston University)

Tue
Mar 23

Team 4

Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong: InFoRM: Individual Fairness on Graph Mining. KDD 2020: 379-389

Fri
Mar 26

Prof. Hanghang Tong
(University of Illinois at Urbana-Champaign)

Tue
Mar 30

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
Apr 2

Dr. Petar Veličković

(DeepMind)

Tue
Apr 6

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
Apr 9

Prof. Leman Akoglu
(Carnegie Mellon University)

Tue
Apr 13

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
Apr 16

Prof. Stephan Günnemann
(Technical University of Munich)

Tue
Apr 20

Team 5

Prathyush Sambaturu, Bijaya Adhikari, B. Aditya Prakash, Srinivasan Venkatramanan, Anil Vullikanti: Designing Effective and Practical Interventions to Contain Epidemics. AAMAS 2020: 1187-1195 (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 COVID-19 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
Apr 23

Prof. B. Aditya Prakash
(Georgia Institute of Technology)

 

Notes, Policies, and Guidelines

o   You are expected to have read the assigned material before each lecture.

o   We will use Northeastern’s Canvas for announcements, assignments, and your contributions.

o   When emailing me, begin the subject line with [sp21 nets].

o   For your class project, you can use whatever programming language that you like.

o   Refresh your knowledge of the university's academic integrity policy and plagiarism. There is zero-tolerance for cheating!