Beyond Bag-of-Words: Harnessing Node2Vec, GraphSAGE, and LLMs for Enhanced Node Embeddings

In this video, we explore cutting-edge techniques for representing nodes in graph data that go beyond the limitations of traditional bag-of-words (BoW) approaches. Discover how to effectively capture network structure and semantic meaning using methods like Node2Vec, GraphSAGE, and language model embeddings (LLMs) such as MPNet. 📈💡
🔑 Key Takeaways:

Understand the limitations of BoW for representing nodes in graph data
Learn how Node2Vec preserves structural roles and similarity in static graphs
Discover how GraphSAGE enables inductive node representation learning for dynamic graphs
See the impact of combining LLM node features with Node2Vec or GraphSAGE on node classification tasks
Get pro tips on tuning parameters, controlling inference time, and balancing embedding influence

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