Date

Lecturer

Readings

Fri Sep 6

Tina EliassiRad

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

Tue Sep 10

Tina EliassiRad

Homophily
·
Distribution of Node
Characteristics in Complex Networks
·
Combinatorial
Characterizations and Impossibilities for Higherorder Homophily
·
Higherorder
Homophily on Simplicial Complexes
Axiomatic Approaches
·
Measuring Tie Strength in
Implicit Social Networks

Fri Sep 13

Tina EliassiRad

Network Comparison and Graph Distances
·
A Guide to Selecting a
Network Similarity Method
·
Network
Comparison and the Withinensemble Graph Distance
·
Nonbacktracking
Cycles: Length Spectrum Theory and Graph Mining Applications
·
[reference]
netrd: A library for Network Reconstruction and Graph
Distances

Tue Sep 17

Tina EliassiRad

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 20

Tina EliassiRad

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 24

Team 1: Sharaj Kunjar, Tamanna Urmi,
Xiyu Yang

Lowrank Representations of Complex Networks
·
The Impossibility of
Lowrank Representations for TriangleRich Complex Networks
·
Node Embeddings and Exact Lowrank
Representations of Complex Networks
·
[optional]
Classic Graph Structural Features
Outperform FactorizationBased Graph Embedding Methods on Community Labeling
·
[optional]
Link Prediction
Using Lowdimensional Node Embeddings: The Measurement Problem

Fri Sep 27

Team 2: Amanuel Tesfaye, Narayan Sabhahit,
Yiyuan Zhang

Graph Neural Networks
·
SemiSupervised
Classification with Graph Convolutional Networks
·
Graph Attention Networks
·
Gated Graph Sequence Neural Networks
·
[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 Oct 1

Team 3: Jesseba Fernando, Joshua “Josh” Rosen, Yicheng “Andy” Zhang

This and That
·
Hyperbolic Graph Convolutional Neural
Networks
· Principal
Neighbourhood Aggregation for Graph Nets
·
Pitfalls of Graph Neural Network
Evaluation
·
[optional]
The Numerical Stability of Hyperbolic Representation Learning

Fri Oct 4

Team 1: Sharaj Kunjar, Tamanna Urmi,
Xiyu Yang

Collective Classification
·
Collective Classification in Network
Data
·
Graph Belief Propagation Networks
·
[optional]
Cautious
Collective Classification

Tue Oct 8

Team 2: Amanuel Tesfaye, Narayan Sabhahit,
Yiyuan Zhang

Label Propagation on Graphs
·
Combining Label Propagation and
Simple Models Outperforms Graph Neural Networks
·
Masked Label
Prediction: Unified Message Passing Model for SemiSupervised Classification
·
Design Space for Graph Neural
Networks (GitHub page)
·
[optional]
Message passing all the way up
Class
project proposals are due at 11:59 PM Eastern.

Fri Oct 11

Team 3: Jesseba Fernando, Joshua “Josh” Rosen,
Yicheng “Andy” Zhang

Graph Transformers
·
Graph Transformer Networks
·
A Generalization of Transformer
Networks to Graphs
·
Transformers
are Graph Neural Networks
·
[reference]
Graph Transformers: A Survey

Tue Oct 15

Team 1: Sharaj Kunjar, Tamanna Urmi,
Xiyu Yang

ML on Heterogeneous Graphs
·
Modeling Relational Data with
Graph Convolutional Networks
·
Heterogeneous Graph Transformer

Fri Oct 18

Team 2:Amanuel Tesfaye, Narayan Sabhahit,
Yiyuan Zhang

GNNs for Recommendation Systems
·
Neural Graph Collaborative
Filtering
·
Graph Convolutional Neural
Networks for WebScale Recommender Systems

Tue Oct 22

Team 3: Jesseba Fernando, Joshua “Josh” Rosen, Yicheng “Andy” Zhang

WL Graph Kernels and Power of GNNs
·
WeisfeilerLehman 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 25

Team 1: Sharaj Kunjar, Tamanna Urmi,
Xiyu Yang

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 29

Team 2: Amanuel Tesfaye, Narayan Sabhahit,
Yiyuan Zhang

Invariance and Equivariance
·
E(n) Equivariant Graph Neural Networks
·
Invariant and Equivariant Graph Networks

Fri Nov 1

Team 3: Jesseba Fernando, Joshua “Josh” Rosen, Yicheng “Andy” Zhang

Hypergraphs and Higherorder Models
·
Random Walks
on Hypergraphs with EdgeDependent Vertex Weights
·
Hypergraph Neural Networks
·
[reference]
A Survey on Hypergraph Mining:
Patterns, Tools, and Generators
· [optional] Simplicial Attention Networks

Tue Nov 5

Team 1: Sharaj Kunjar, Tamanna Urmi,
Xiyu Yang

Graph ML for Optimization
·
Assigning Entities to Teams as a
Hypergraph Discovery Problem
·
Distributed
constrained combinatorial optimization leveraging hypergraph neural networks

Fri Nov 8

Guest Lecturer:
Alesia Chernikova

Cyber networks and ML
·
Cyber Network Resilience against
SelfPropagating Malware Attacks
·
Modeling SelfPropagating Malware
with Epidemiological Models

Tue Nov 12

Team 2: Amanuel Tesfaye, Narayan Sabhahit,
Yiyuan Zhang

Explainability in GNNs
·
Explainability in Graph Neural
Networks: A Taxonomic Survey
·
GNNExplainer:
Generating Explanations for Graph Neural Networks

Fri Nov 15

Team 3: Jesseba Fernando, Joshua “Josh” Rosen, Yicheng “Andy” Zhang

Explainability and Trustworthiness of GNNs
·
GraphFramEx:
Towards Systematic Evaluation of Explainability Methods for Graph Neural
Networks
·
Trustworthy Graph Neural
Networks: Aspects, Methods and Trends

Tue Nov 19

Team 1: Sharaj Kunjar, Tamanna Urmi,
Xiyu Yang

Fairness and Equality
·
FAIRGEN: Towards Fair Graph
Generation
·
Information
Access Equality on Generative Models of Complex Networks

Fri Nov 22

Team 2: Amanuel Tesfaye, Narayan Sabhahit,
Yiyuan Zhang

Oversmooting and Oversquashing
·
A Survey on Oversmoothing
in Graph Neural Networks
·
How does oversquashing affect the
power of GNNs?
·
Neural Sheaf Diffusion: A Topological
Perspective on Heterophily and Oversmoothing in
GNNs

Tue Nov 26

Team 3: Jesseba Fernando, Joshua “Josh” Rosen, Yicheng “Andy” Zhang

ICML 2024 Position Papers
·
Future Directions in the
Theory of Graph Machine Learning
·
Why We Must Rethink Empirical
Research in Machine Learning

Fri Nov 29

No
class – Thanksgiving break

Tue Dec 3

Guest lecturer:
Germans Savcisens

ML for Computational Social Science
·
Using
Sequences of Lifeevents to Predict Human Lives
(Website: https://www.life2vec.dk)

Fri Dec 6

Inclass project
presentations

Your
slides are due at 12pm Eastern.

Tue Dec 10

No
class – Project reports are due on Tue Dec 10 at 11:59pm Eastern.

Fri Dec 13

No
class
