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08 Document Network Analysis

Source: notebooks/08-document-network-analysis-graphistry-e1.ipynb

Notebook focus

This page is a cell-by-cell walkthrough of the notebook, explaining the intent of each step and showing the exact code executed.

Cell-by-cell walkthrough

Cell 1 (Markdown)

08 Document Network Analysis

Build document similarity networks with GraphistryBuilders.document_similarity().

What you will learn: - Model documents as nodes with metadata - Define pairwise similarity scores as edges - Generate DataFrames for graph visualization

Requirements: llamatelemetry installed. No GPU required.

Cell 2 (Markdown)

1) Install

Cell 3 (Code)

Summary: Installs required dependencies and runtime tools.

!pip -q install git+https://github.com/llamatelemetry/llamatelemetry.git@v0.1.1

Cell 4 (Markdown)

2) Define documents and similarity scores

Cell 5 (Code)

Summary: Imports core libraries: llamatelemetry. Sets up Graphistry for graph visualization or analytics.

from llamatelemetry.graphistry import GraphistryBuilders

documents = [
    {"id": "doc1", "title": "CUDA Programming Guide"},
    {"id": "doc2", "title": "llama.cpp Server Architecture"},
    {"id": "doc3", "title": "OpenTelemetry Metrics Specification"},
    {"id": "doc4", "title": "NCCL Collective Operations"},
    {"id": "doc5", "title": "GPU Memory Management"},
]

similarities = [
    {"source": "doc1", "target": "doc2", "score": 0.72},
    {"source": "doc1", "target": "doc4", "score": 0.85},
    {"source": "doc1", "target": "doc5", "score": 0.91},
    {"source": "doc2", "target": "doc3", "score": 0.68},
    {"source": "doc4", "target": "doc5", "score": 0.77},
]

Cell 6 (Markdown)

3) Build the similarity graph

Cell 7 (Code)

Summary: Executes notebook-specific logic or data processing for this step.

nodes_df, edges_df = GraphistryBuilders.document_similarity(documents, similarities)

print("=== Document Nodes ===")
print(nodes_df.to_string(index=False))
print(f"\n=== Similarity Edges ({len(edges_df)}) ===")
print(edges_df.to_string(index=False))

Cell 8 (Markdown)

4) Filter by threshold

Cell 9 (Code)

Summary: Executes notebook-specific logic or data processing for this step.

threshold = 0.75
strong_edges = edges_df[edges_df["score"] >= threshold]
print(f"Edges with score >= {threshold}:")
print(strong_edges.to_string(index=False))

Cell 10 (Markdown)

5) Visualize (optional)

Cell 11 (Code)

Summary: Sets up Graphistry for graph visualization or analytics.

# from llamatelemetry.graphistry import GraphistrySession
# session = GraphistrySession.from_kaggle_secrets()
# g = session.connector.create_graph(edges_df, nodes_df=nodes_df)
# g.plot()