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