Fall 2025: Machine Learning with Graphs – CS 7332 (CRN 18734) & NETS 7332 (CRN 17263)

 

General Information

Time: Tuesdays & Fridays 1:35 – 3:15 PM Eastern

Place: 177 Huntington Ave, 2nd Floor Conference Room (#207)

Instructor: Tina Eliassi-Rad

Course website on Canvas: https://northeastern.instructure.com/courses/230262

Office hours: Available by appointment. Email eliassi [at] ccs [dot] neu [dot] edu
to setup an appointment; begin the subject line with [fa25 nets].

TA: Samantha “Sam” Dies. Available by appointment. Email dies [dot] s [at]
northeastern [dot] edu to setup an appointment; begin the subject line with [fa25 nets].

 

Overview

This 4-credit PhD-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, C++, Java, Ruby, 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 (40%)

o   I will team up students into groups.

o   Each week will have 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   Slides for lecture presentations are due at 12:00 PM (noon) Eastern on the day assigned to your team.

o   Class project (50%)

o   Each team will choose (by Tuesday, October 7, 2025 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://media.neurips.cc/Conferences/NeurIPS2025/Styles.zip.

o   Reports on class projects are due on Friday, December 12, 2025 at 11:59 PM Eastern.

o   Slides for class project (10%)

o   Slides on class projects are due on Friday, December 12, 2025 at 11:59 PM Eastern.

 

Schedule/Syllabus (Subject to Change)

Date

Lecturer

Readings

Fri Sep 5

Tina Eliassi-Rad

Overview

·      The Why, How, and When of Representations for Complex Systems

Tue Sep 9

Tina Eliassi-Rad

Homophily

·      Distribution of Node Characteristics in Complex Networks

·      Combinatorial Characterizations and Impossibilities for Higher-order Homophily

·      Higher-order Homophily on Simplicial Complexes

Axiomatic Approaches

·      Measuring Tie Strength in Implicit Social Networks 

Fri Sep 12

Tina Eliassi-Rad

Network Comparison and Graph Distances

·      A Guide to Selecting a Network Similarity Method 

·      Network Comparison and the Within-ensemble Graph Distance

·      Non-backtracking Cycles: Length Spectrum Theory and Graph Mining Applications

·      [reference] netrd: A library for Network Reconstruction and Graph Distances

Tue Sep 16

Tina Eliassi-Rad

Role Discovery

·      It's Who You Know: Graph Mining Using Recursive Structural Features

·      RolX: Structural Role Extraction & Mining in Large Graphs

·      Guided learning for Role Discovery (GLRD): Framework, Algorithms, and Applications

Fri Sep 19

Tina Eliassi-Rad

Graph Representation Learning: Node Embedding

·      Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

·      node2vec: Scalable Feature Learning for Networks

·      Structural Deep Network Embedding

·      STABLE: Identifying and Mitigating Instability in Embeddings of the Degenerate Core

·      [reference] Machine Learning on Graphs: A Model and Comprehensive Taxonomy

·      [reference] Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding

·      [optional] Next Waves in Veridical Network Embedding

Tue Sep 23

Team 1

Low-rank Representations of Complex Networks

·      The Impossibility of Low-rank Representations for Triangle-Rich Complex Networks

·      Node Embeddings and Exact Low-rank Representations of Complex Networks

·      [optional] Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community Labeling

·      [optional] Link Prediction Using Low-dimensional Node Embeddings: The Measurement Problem

Fri Sep 26

Team 2

Graph Neural Networks

·      Semi-Supervised Classification with Graph Convolutional Networks

·      Graph Attention Networks

·      Inductive Representation Learning on Large Graphs

·      [reference] Graph Neural Networks: A Review of Methods and Applications

·      [reference] A Comprehensive Survey on Graph Neural Networks

·      [optional] Everything is Connected: Graph Neural Networks

Tue Sep 30

Team 3

This and That

·      Hyperbolic Graph Convolutional Neural Networks

·      Pitfalls of Graph Neural Network Evaluation

·      Design Space for Graph Neural Networks (GitHub page)

·      [optional] The Numerical Stability of Hyperbolic Representation Learning

Fri Oct 3

Team 4

Collective Classification

·      Collective Classification in Network Data

·      Graph Belief Propagation Networks

·      [optional] Cautious Collective Classification

Tue Oct 7

Team 5

Label Propagation on Graphs

·      Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

·      Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

·      [optional] Message passing all the way up

Class project proposals are due at 11:59 PM Eastern.

Fri Oct 10

Team 6

GNNs for Recommendation Systems

·      LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

·      Neural Graph Collaborative Filtering

·      Graph Convolutional Neural Networks for Web-Scale Recommender Systems

·      [reference] Graph Neural Networks in Recommender Systems: A Survey

·      [reference] A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

Tue Oct 14

Team 7

Hypergraphs and Higher-order Models with applications to Graph ML for Optimization

·      Assigning Entities to Teams as a Hypergraph Discovery Problem

·      Distributed constrained combinatorial optimization leveraging hypergraph neural networks

·      [reference] A Survey on Hypergraph Mining: Patterns, Tools, and Generators

·      [optional] Random Walks on Hypergraphs with Edge-Dependent Vertex Weights

·      [optional] Hypergraph Neural Networks

·      [optional] Simplicial Attention Networks

Fri Oct 17

Team 1

Oversmooting and Oversquashing

·      A Survey on Oversmoothing in Graph Neural Networks

·      How does over-squashing affect the power of GNNs?

·      Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs

Tue Oct 21

Team 2

W-L Graph Kernels and Power of GNNs

·      Weisfeiler-Lehman Graph Kernels

·      How Powerful are Graph Neural Networks?

·      [optional] A Reduction of a Graph to a Canonical Form and an Algebra arising during this Reduction

·      [reference] Theory of Graph Neural Networks: Representation and Learning

Fri Oct 24

Team 3

Stability and Counting in GNNs

·      Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks

·      Can Graph Neural Networks Count Substructures?

Tue Oct 28

Team 4

Invariance and Equivariance

·      E(n) Equivariant Graph Neural Networks

·      Invariant and Equivariant Graph Networks

Fri Oct 31

Team 5

*-aware GNNs

·      Position-aware Graph Neural Networks

·      Identity-aware Graph Neural Networks

Tue Nov 4

Team 6

This and That

·      PRODIGY: Enabling In-context Learning Over Graphs 

·      Uncertainty Quantification over Graph with Conformalized Graph Neural Networks

·      REGE: A Method for Incorporating Uncertainty in Graph Embeddings

Fri Nov 7

Guest Lecturer:
Germans Savcisens

ML for Computational Social Science

Using Sequences of Life-events to Predict Human Lives
(Website: https://www.life2vec.dk)

Tue Nov 11

No class – Veterans Day

 

Fri Nov 14

Team 7

Explainability in GNNs

·      Explainability in Graph Neural Networks: A Taxonomic Survey

·      GNNExplainer: Generating Explanations for Graph Neural Networks

Tue Nov 18

Team 1

(Moderator: Samantha Dies)

Explainability and Trustworthiness of GNNs

·      GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks

·      Trustworthy Graph Neural Networks: Aspects, Methods and Trends

Fri Nov 21

Team 2

Fairness and Equality

·      FAIRGEN: Towards Fair Graph Generation

·      Information Access Equality on Generative Models of Complex Networks

Tue Nov 25

Team 3

Graph Transformers I

·      Graph Transformer Networks

·      A Generalization of Transformer Networks to Graphs

·      Transformers are Graph Neural Networks

·      [reference] Graph Transformers: A Survey

Fri Nov 28

No class – Thanksgiving break 

Tue Dec 2

Team 4

Graph Transformers II

Fri Dec 5

Team 5

ML on Heterogeneous Graphs

·      Modeling Relational Data with Graph Convolutional Networks

·      Heterogeneous Graph Transformer

Tue Dec 9

Team 6

(Moderator: Alesia Chernikova)

Circuit Tracing in Large Language Models

·      Circuit Tracing: Revealing Computational Graphs in Language Models

·      Tracing Attention Computation Through Feature Interactions

Fri Dec 12

Team 7

Recent Position Papers

·      Future Directions in the Theory of Graph Machine Learning

·      Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

·      Why We Must Rethink Empirical Research in Machine Learning

Slides for class projects are due at 11:59 PM Eastern.

Reports on class projects are due at 11:59 PM Eastern.

 

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 [fa25 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!