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Hardcore Project for Internships
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Module 6
HNSW: Hierarchical Structure and Search
1
HNSW: Hierarchical Graph for Faster Search
Explain how HNSW improves upon NSW by introducing a hierarchical, multi-layer graph structure.
2
Probabilistic Layer Assignment & Skip Lists
Describe the probabilistic layer assignment for new nodes, drawing parallels to skip lists.
3
HNSW Node Design for Variable Connections and Multi-Layer Support
Design the in-memory data structures for an HNSW graph node, supporting a variable number of connections on multiple layers.
4
Implementing SEARCH-LAYER for k-NN in a Single Graph Layer
Implement the `SEARCH-LAYER` algorithm for finding `k` nearest neighbors within a single layer of the graph.
5
Top-Down Search for Optimal Entry Points
Implement the top-level search algorithm that traverses from the top layer down to find optimal entry points for lower layers.
6
Optimizing k-NN Search with `efSearch`
Combine the search algorithms to perform a full k-NN search, using the `efSearch` parameter to control the candidate list size.
Previous module
ANN with Navigable Small World (NSW) Graphs
Next module
HNSW: Insertion and Graph Construction