Date

Lecturer

Readings

Fri Sep 8

Tina EliassiRad

Overview

Tue Sep 12

Tina EliassiRad

Various Representations
· The Why, How, and
When of Representations for
Complex Systems
·
Nonbacktracking
Cycles: Length Spectrum Theory and Graph Mining Applications

Fri Sep 15

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

Tue Sep 19

Ayan Chatterjee

Graph Machine Learning for Drug Discovery + Graph
Machine Learning Benchmarks
·
AIBind: Improving Binding Predictions for Novel
Protein Targets and Ligands
·
Disentangling Node Attributes from
Graph Topology for Improved Generalizability in Link Prediction
·
OGB:
Open Graph Benchmark
·
DGL:
Deep Graph Library
·
TGB:
Temporal Graph Benchmark
·
GraphWorld

Fri Sep 22

Brennan Klein

Network Comparison and Graph Distances
·
Network
Comparison and the Withinensemble Graph Distance
·
netrd:
A library for Network Reconstruction and Graph Distances

Tue Sep 26

David Liu

Node Embedding
·
STABLE: Identifying and
Mitigating Instability in Embeddings of the Degenerate Core
·
Laplacian
Eigenmaps for Dimensionality Reduction and Data Representation
·
node2vec:
Scalable Feature Learning for Networks
·
Structural
Deep Network Embedding
·
Machine Learning on Graphs: A Model
and Comprehensive Taxonomy

Fri Sep 29

Team 1
Alyssa Smith & Mel
Allen & Remy LeWinter

More on Representation Learning
·
Deep Graph Infomax
·
Graph Representation Learning via
Graphical Mutual Information Maximization
·
[optional]
Network Representation Learning:
From Preprocessing, Feature Extraction to Node Embedding

Tue Oct 3

Team 2
Joey Ehlert & Mortiz Laber & Sam Dies

Lowrank Representations of Complex Networks
·
The Impossibility of
LowRank Representations for TriangleRich Complex Networks
·
Node Embeddings and Exact LowRank
Representations of Complex Networks

Fri Oct 6

Team 3
Julian Gullett & Sagar
Kumar & Yixuan Liu

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

Tue Oct 10

Team 1
Alyssa Smith & Mel
Allen & Remy LeWinter

Graph Neural Networks
·
Graph Neural Networks: A Review of
Methods and Applications
·
Transformers
are Graph Neural Networks
·
[optional]
Everything is Connected: Graph
Neural Networks

Fri Oct 13

Team 2
Joey Ehlert & Mortiz Laber & Sam Dies

This and That (I)
·
A Generalization of Transformer
Networks to Graphs
·
Hyperbolic Graph Convolutional Neural
Networks

Tue Oct 17

Tina EliassiRad

Label Propagation on Graphs
·
Combining Label Propagation and
Simple Models Outperforms Graph Neural Networks
·
Masked Label
Prediction: Unified Message Passing Model for SemiSupervised Classification
·
[optional]
Message passing all the way up

Fri Oct 20

Team 3
Julian Gullett & Sagar
Kumar & Yixuan Liu

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

Tue Oct 24

Team 1
Alyssa Smith & Mel
Allen & Remy LeWinter

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

Fri Oct 27

Team 2
Joey Ehlert & Mortiz Laber & Sam Dies

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 31

Team 3
Julian Gullett & Sagar
Kumar & Yixuan Liu

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

Fri Nov 3

Team 1
Alyssa Smith & Mel
Allen & Remy LeWinter

Ayan
Chatterjee will emcee this lecture.
Hypergraphs
·
Random Walks on
Hypergraphs with EdgeDependent Vertex Weights
·
Hypergraph Neural Networks

Tue Nov 7

Team 2
Joey Ehlert & Mortiz Laber & Sam Dies

David Liu will emcee this lecture.
Explainability in
GNNs
·
Explainability
in Graph Neural Networks: A Taxonomic Survey
·
GNNExplainer:
Generating Explanations for Graph Neural Networks

Fri Nov 10

Team 3
Julian Gullett & Sagar
Kumar & Yixuan Liu

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

Tue Nov 14

Team 1
Alyssa Smith & Mel
Allen & Remy LeWinter

This and That (I)
· Principal
Neighbourhood Aggregation for Graph Nets
·
How does oversquashing affect the
power of GNNs?

Fri Nov 17

Team 2
Joey Ehlert & Mortiz Laber & Sam Dies

Oversmooting
·
A Survey on Oversmoothing
in Graph Neural Networks
·
Neural Sheaf Diffusion: A Topological
Perspective on Heterophily and Oversmoothing in
GNNs

Tue Nov 21

Team 3
Julian Gullett & Sagar
Kumar & Yixuan Liu

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

Fri Nov 24

No
class – Thanksgiving break

Tue Nov 28

Team 1
Alyssa Smith & Mel
Allen & Remy LeWinter

Learning on Signed Networks
·
Learning Signed Network
Embedding via Graph Attention
·
Signed Graph Attention Networks

Fri Dec 1

Team 2
Joey Ehlert & Mortiz Laber & Sam Dies

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

Tue Dec 5

Team 3
Julian Gullett & Sagar
Kumar & Yixuan Liu

*aware GNNs
·
Positionaware Graph Neural
Networks
·
Identityaware Graph Neural
Networks

Fri Dec 8

Tina EliassiRad

This
lecture will be on Zoom.
This and That (II)
·
Pitfalls of Graph Neural Network
Evaluation
·
Classic Graph Structural Features Outperform
FactorizationBased Graph Embedding Methods on Community Labeling

Tue Dec 12

No
class – NetSI Qualifying Exam Week

Fri Dec 15

Project Presentations

