Study self-supervised objectives that learn graph representations from structure, relations, and unlabeled examples.
Explore self-supervised graph representation learning through adaptive contrastive augmentation, multi-channel hypergraph modeling, contextual embeddings for heterogeneous link prediction, and logical-query representations in knowledge graphs. The course compares what different pretext signals make learnable and how graph type, augmentation, geometry, and downstream evaluation shape the result. Learners design leakage-aware tests rather than treating an unlabeled objective as evidence of general-purpose representations. English-audio videos are paired with independently written study materials, not transcripts.
Compare contrastive, hypergraph, link-prediction, and logical-query supervision signals in terms of what graph structure each encourages a representation to preserve. Assess whether an augmentation or graph geometry is appropriate for the downstream relation and data-generating process. Design graph representation evaluations with held-out entities, edges, queries, or graphs as appropriate, and test for pretraining–evaluation leakage.
Prior familiarity with neural networks, graph data, embeddings, and supervised learning is recommended. Comfort with adjacency structures, relational data, and evaluation splits will help; advanced topology or a particular graph framework is not required.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
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