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Document Network Analysis with Graphistry

Analyze document similarity and topic clustering using GPU-accelerated graph analytics.

Level: Advanced | Time: 35 minutes | VRAM Required: GPU 0: 6-10 GB, GPU 1: 3-5 GB

Overview

This notebook demonstrates how to analyze document similarity and topic clustering using GPU-accelerated graph analytics with RAPIDS cuGraph and interactive visualization with Graphistry.

Key Features

  • Document embedding generation via LLM
  • Similarity network construction (cosine similarity)
  • Community detection using RAPIDS cuGraph
  • Topic clustering with GPU-accelerated algorithms
  • Interactive visualization with Graphistry
  • Dual-GPU workflow (embeddings on GPU 0, analytics on GPU 1)

Key Algorithms

  • Louvain community detection - Find document clusters
  • PageRank - Identify influential documents
  • Betweenness centrality - Find bridge documents
  • K-core decomposition - Extract dense subnetworks

Applications

  • Research paper citation networks
  • News article topic analysis
  • Corporate document organization
  • Social media content clustering

Workflow

# 1. Generate embeddings
embeddings = get_embeddings_from_llm(documents)

# 2. Build similarity graph
similarity_matrix = cosine_similarity(embeddings)
graph = build_graph_from_similarity(similarity_matrix, threshold=0.7)

# 3. GPU analytics with cuGraph
communities = cugraph.louvain(graph)
pagerank = cugraph.pagerank(graph)

# 4. Visualize
g = graphistry.nodes(docs_df).edges(edges_df)
g.plot()

Open in Kaggle

Kaggle