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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 (13 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 104

Skills You Will Learn

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

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • January 28, 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.

Instructors

Zikai Dou

Data Scientist at IBM

Ph.D. Candidate in Computer Science at McMaster University, specializing in Federated Learning (FL), Graph Neural Networks (GNNs), and Computer Vision (CV). I develop privacy-preserving, distributed AI systems that tackle real-world challenges in healthcare, finance, and enterprise applications. Passionate about bridging academic research with industry impact to advance scalable and trustworthy AI.

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Contributors

Wojciech "Victor" Fulmyk

Data Scientist at IBM

Wojciech "Victor" Fulmyk is a Data Scientist and AI Engineer on IBM’s Skills Network team, where he focuses on helping learners build expertise in data science, artificial intelligence, and machine learning. He is also a Kaggle competition expert, currently ranked in the top 3% globally among competition participants. An economist by training, he applies his knowledge of statistics and econometrics to bring a distinctive perspective to AI and ML—one that considers both technical depth and broader socioeconomic implications.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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