Learning Path

Graph Machine Learning: From Representations to Link Prediction

Graph Theory Fundamentals → Graph ML Implementation and Evaluation

13 Knowledge Nodes · 13 Learning Steps · v0 Active

A comprehensive graduate-level path for practitioners to apply machine learning to graph-structured data. Starting from graph theory fundamentals and classical embedding methods, it progresses through modern graph neural network architectures (GCN, GraphSAGE, GAT) and culminates in advanced link prediction techniques. The path emphasizes hands-on implementation and evaluation, preparing learners to design and deploy graph ML solutions in real-world scenarios.

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13 nodes23 edges3 relationships

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Learning Route

13 learning steps · 3 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

5 steps

Phase 2

Core Concepts

4 steps

Phase 3

Advanced Topics

4 steps