What will you learn in Introduction to Graph Machine Learning Course
Create and manipulate graph structures for data analysis.
Understand graph embedding techniques: matrix factorization, random walks, and neural methods.
Formulate and solve graph analytics tasks such as node classification, link prediction, and clustering.
Build and train Graph Neural Networks (GNNs) using PyTorch Geometric.
Construct and embed knowledge graphs, mastering translation-, factorization-, and neural-based methods.
Program Overview
Module 1: About the Course
⏳ 0.5 hours
Topics: Course introduction, scope, tools, and prerequisites.
Hands-on: Set up your Python and PyTorch Geometric environment.
Module 2: Introduction to Graph Theory
⏳ 0.75 hours
Topics: Definitions of graphs, types of graphs, data-structure representations, visualization.
Hands-on: Create simple graph objects and visualize them.
Module 3: Graph Embeddings
⏳ 1 hour
Topics: Matrix factorization, random-walk approaches, neural embedding techniques.
Hands-on: Generate embeddings with each method and inspect vector relationships.
Module 4: Supervised and Unsupervised Graph ML
⏳ 1 hour
Topics: Node classification, link prediction, graph classification, clustering, community detection.
Hands-on: Implement and evaluate each graph-analytics task.
Module 5: Graph Neural Networks
⏳ 0.75 hours
Topics: GNN architectures, message-passing paradigm, popular GNN variants.
Hands-on: Build a GNN model in PyTorch Geometric and run a training loop.
Module 6: Knowledge Graphs
⏳ 1 hour
Topics: Knowledge-graph construction, schema challenges, and use-cases.
Hands-on: Assemble a simple knowledge graph from sample data.
Module 7: Knowledge Graph Embeddings
⏳ 0.75 hours
Topics: Translation-based, factorization-based, and neural embedding methods.
Hands-on: Train and compare embedding approaches on a knowledge graph.
Module 8: Case Study: Link Prediction on a Social Network
⏳ 0.5 hours
Topics: Problem framing, modeling approach, and evaluation metrics.
Hands-on: Code a link-prediction solution end-to-end.
Module 9: Case Study: Node Classification on a Biological Graph
⏳ 0.5 hours
Topics: Biological-network characteristics and classification challenges.
Hands-on: Implement a GNN for node classification on a contact-tracing graph.
Module 10: Appendix
⏳ 0.25 hours
Topics: Python libraries and version requirements for graph ML.
Hands-on: Verify and document your environment setup.
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Job Outlook
Graph-ML expertise is increasingly sought in tech, finance, biotech, and social-media companies.
Roles include Graph Data Engineer, Machine Learning Engineer, and Data Scientist specializing in networked data.
Professionals with GNN and knowledge-graph skills command salaries from $100K–$140K (USD), with higher ranges in major tech hubs.
Graph ML opens opportunities in recommendation systems, fraud detection, drug discovery, and knowledge-management applications.
Specification: Introduction to Graph Machine Learning
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