GraphMachineLearning: A Jupyter Notebook repository from Ultraviolet-Ninja

Graph Machine Learning

This course provides an introduction to machine learning on graphs.

Many important real-world datasets can be represented as a graph of relationships between objects. Such networks are a basic tool for modeling social networks, knowledge graphs, the Web, and biological systems such as protein-interaction networks. Until recently, very little attention has been devoted to the generalization of neural network models to such structured datasets.

This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of graphs. By studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools better able to reveal insights on a variety of networks.

Fundamental questions:
Can we take advantage of graph structure to learn better representations of data? Given better representations, can we make better predictions?

Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.

Lectures are augmented with hands-on tutorials using Jupyter Notebooks. Laboratory assignments will be completed using Python and related packages: PyTorch, PyG, NumPy, Pandas, SciPy, StatsModels, SciKit-Learn, NetworkX, and MatPlotLib.

2-2-3 (class hours/week, laboratory hours/week, credits)

Prerequisites: CS-385 Algorithms, Probability and Statistics; programming maturity, and the ability to program in Python.

ABET: Math/Science, Engineering Topics.

Outcomes:

Understand the basic process of applying machine learning to graph data.
The ability to identify, load, and prepare a graph data set for a given problem.
The ability to analyze a data set including the ability to understand which data attributes (dimensions) affect the outcome.
The ability to develop and apply graph neural network algorithms for node classifcation, link detection, community detection, and graph generation.
The ability to apply methods to real world data sets.
The ability to identify, articulate, and propose a research problem related to graph machine learning.

Tools: Python and related packages for data analysis, machine learning, and visualization. Jupyter Notebooks.

Grading:
Weekly labs and final project: 60%
Midterm: 20%
Final: 20%

Office DH425:
T 3-4pm, Th 3-4pm

References:

Graph Representation Learning by William L. Hamilton

http://www.cs.cornell.edu/home/kleinber/networks-book/Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg

Network Science by Albert-László Barabási

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković

Geometric Deep Learning

Stanford Machine Learning on Graphs

PyG - Pytorch Geometric Documentation