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Agentic Graph-RAG Over Social-Network Knowledge Graphs

IntermediateGuided Project

Learn how to build an AI agent that retrieves, ranks, and summarizes information from a social-network graph. This guided project introduces a lightweight Graph-RAG workflow and demonstrates how an agent can combine graph structure, ranking logic, and AI reasoning to generate clear, data-driven insights. By working through each step, you will gain practical experience with graph-based retrieval and understand how modern AI systems navigate and interpret connected data. You will also learn how each component works together in an end-to-end agentic pipeline, giving you stronger foundation.

4.8 (40 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 352

Skills You Will Learn

  • AI Agents, Graph Neural Networks (GNNs), Knowledge Graphs, LangGraph, Pydantic, Retrieval-Augmented Generation (RAG)

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • March 17, 2026
About this Guided Project
In this project, you will learn how modern AI agents can work with graph-structured data to retrieve, rank, and summarize information. You will build a lightweight Graph-RAG workflow where an agent explores a social-network graph, identifies influential nodes, and generates clear insights using an LLM. By following the step-by-step process, you will see how graph structure, ranking logic, and agent reasoning come together in a practical, end-to-end retrieval pipeline.

Who Is It For

This project is designed for learners with foundational Python skills, such as software engineers, data scientists, or AI practitioners, who want to deepen their understanding of how modern AI systems retrieve, rank, and summarize information using lightweight agent workflows. This project is designed for individuals who want to move beyond basic retrieval-augmented generation (RAG) methods and gain practical, hands-on experience with graph-based reasoning and structured AI retrieval pipelines.

What You’ll Learn

By the end of this project, you will understand how AI agents navigate connected data, how Graph-RAG differs from traditional RAG, and how simple graph features can significantly improve the quality of retrieved information. You will be able to:
  • Learn how to construct and analyze a social-network graph and extract meaningful subgraphs for retrieval.
  • Build an AI agent that retrieves graph data, applies ranking logic, and produces structured explanations with the help of an LLM.

What You'll Need

You should be comfortable writing basic Python code and working with common data structures. No prior experience with AI agents or Graph-RAG is required. The libraries used in this project can be installed directly within the IBM Skills Network Labs environment, allowing you to set up and run the workflow without any external configuration. The project works best on modern browsers such as Chrome, Edge, Firefox, or Safari.