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:

• Charu C. Aggarwal. Linear Algebra and Optimization for Machine Learning: A Textbook. Springer, 2020.
• Christos Faloutsos, Deepayan Chakrabarti. Graph Mining: Laws, Tools, and Case Studies. Morgan & Claypool Publishers, 2012.
• Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. Springer, 2nd edition, 2009. (free online)
• Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
• Peter Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, 2012.
• Tom Mitchell. Machine Learning. McGraw Hill, 1997.
• Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.
• Charu C. Aggarwal. Neural Networks and Deep Learning: A Textbook. Springer, 2018.
• Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2016.
• Charu C. Aggarwal. Data Mining, The Textbook. Springer 2015.
• Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd edition, 2011.
• David J. Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining. A Bradford Book, 2001.
• Anand Rajaraman, Jurij Leskovec, and Jeffrey Ullman. Mining of Massive Datasets. Cambridge University Press, v2.1, 2014. (free online) (Errata)
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Pearson, 2nd edition, 2018.

 

Resources

• Probability Review (David Blei, Princeton)
• Probability Theory Review (Arian Maleki and Tom Do, Stanford)
• Probability, Linear Algebra, and Differentiation (Iain Murray)
• Linear Algebra Tutorial (C.T. Abdallah, Penn)
• Linear Algebra Review and Reference (Zico Kolter and Chuong Do, Stanford)
• Theoretical CS Cheat Sheet (Princeton)
• Encyclopedia of Distances (Michel Marie Deza and Elena Deza)
• Mathworks Matlab Tutorials
• Ben Taskar's Matlab Tutorial

 

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 13, 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 ACM formatting guidelines.

o   Reports are due on Saturday April 24, 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. CoRR abs/1909.07294v2 (2020)
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. CoRR abs/2009.13566 (2020)
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 (2020)
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	To be determined
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!