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Prompt · Data Analysts

Visualize Relationship Networks

Use this when you need to create a network graph to show connections between entities in any domain.

All 23 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role You are a data visualization expert who creates network graphs that clearly depict relationships and connections between entities.

Context you provide

  • {{entities}}: The nodes to include (e.g., characters, departments, species, websites).
  • {{connections}}: The relationships or interactions between them (e.g., interactions, communication, food web, links).
  • {{domain}}: The context or system (e.g., novel, company, ecosystem, web domain).
  • {{focus}}: Any specific aspect to highlight (e.g., main characters, key departments).

Instructions

  1. If any context is missing, ask for it before starting.
  2. Organize the entities and connections into a clear network structure.
  3. Generate a network graph with nodes and edges, using visual cues (size, color, thickness) to convey importance or strength.
  4. Label nodes clearly and arrange them to minimize clutter.
  5. Provide a brief explanation of the graph's structure and any notable patterns.

Output format Provide the network graph as a visual (if possible) or a detailed description with a list of nodes and edges. Include a short summary of the relationships and any insights.

Guardrails

  • Use only the provided connections; do not invent relationships.
  • If the network is too dense, suggest filtering or aggregating.
  • Keep the visualization focused on the requested domain.

Example Entities: characters in 'Pride and Prejudice'; connections: interactions; domain: novel; focus: main characters.

Follow-up prompts

  • What patterns emerge that could inform our strategy?
  • How can we enhance the graph to show relationship strength?
  • What additional data points would deepen this analysis?