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Home / Syllabus / B.E. (Artificial Intelligence and Data Science) / Fourth Year BE AI and DS / Semester 8 / Graph Data Science Lab

B.E. (Artificial Intelligence and Data Science) Graph Data Science Lab Syllabus - Mumbai University

This is the Fourth Year BE AI and DS syllabus under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has published no NEP 2020 syllabus for Semesters V to VIII of any engineering branch, so this is the scheme you are examined on — exam form 1T01817 and 1T01818. The first and second years of the degree are on NEP 2020.

Graph Data Science Lab.pdf
Semester 8 · Fourth Year BE AI and DS · 1 credits · CBCS REV-2019 'C' Scheme

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Syllabus for Graph Data Science Lab

Semester 8 · Fourth Year BE AI and DS · 1 credits · CBCS REV-2019 'C' Scheme

Suggested List of Experiments

  • 1 Graph Database Fundamentals: ○ Install and set up a graph database system (e.g., Neo4j) on a local machine. ○ Familiarize yourself with the graph database environment, including the query language (Cypher) and browser interface.
  • 2 Data Modeling with Graphs: ○ Design a data model using the labeled property graph model for a specific domain (e.g., social network, e-commerce). ○ Implement the data model in the graph database and populate it with sample data. 3, Basic Graph Queries: ○ Perform basic graph queries using Cypher to retrieve nodes, relationships, and their properties. ○ Explore different query patterns, such as finding paths, filtering nodes, and ordering results.
  • 4 Advanced Graph Queries: ○ Extend your query knowledge by performing more complex graph queries, including subgraph matching, aggregation, and conditional filtering. ○ Optimize query performance by understanding and utilizing indexes.
  • 5 Graph Database Administration: ○ Learn and practice essential administrative tasks, such as managing users, roles, and access control. ○ Perform backup and restore operations to ensure data integrity.
  • 6 Importing and Exporting Data: ○ Import data from external sources (e.g., CSV files) into the graph database. ○ Export graph data to different formats for analysis or sharing.
  • 7 Graph Algorithms and Analytics: ○ Explore the built-in graph algorithms provided by the graph database system (e.g., centrality, community detection). ○ Apply graph algorithms to analyze and extract insights from your graph data
  • 8 Graph Visualization and Exploration: ○ Utilize visualization tools and libraries to visualize your graph data. ○ Explore and navigate the graph visually to gain a better understanding of its structure and relationships.
  • 9 Performance Optimization: ○ Identify and address performance bottlenecks in your graph database application. ○ Optimize queries, indexes, and data modeling to improve overall system performance.
  • 10 Scaling and Replication: ○ Learn techniques for scaling and replicating a graph database to handle larger datasets and higher workloads. ○ Implement and test replication strategies to ensure data availability and fault tolerance. *11. Real-World Use Cases: ○ Choose a specific real-world use case (e.g., recommendation systems, fraud detection) and apply graph database techniques to solve the problem. ○ Design and implement a graph database application that addresses the unique requirements of the chosen use case.

Text Books

  • 1 Introduction to Graph Theory Fourth edition, Robin J. Wilson
  • 2 Daphne Koller and Nir Friedman, "Probabilistic Graphical Models: Principles and Techniques", Cambridge, MA: The MIT Press, 2009 (ISBN 978-0-262-0139- 2).
  • 3 Graph databases, Ian Robinson, Jim Webber & Emil Eifrem

References

  • 1 "Graph Databases: New Opportunities for Connected Data" by Ian Robinson, Jim Webber, and Emil Eifrém.
  • 2 "Neo4j in Action" by Aleksa Vukotic, Nicki Watt, and Tareq Abedrabbo.
  • 3 "Graph Databases for Beginners" by Mark Needham and Amy E. Hodler.
  • 4 "Practical Neo4j" by Gregory Jordan.
  • 5 "Learning Neo4j" by Rik Van Bruggen.
  • 6 "Graph Database Applications and Concepts with Neo4j" by Dionysios Synodinos.

Digital Material

  • 1 https://web4.ensiie.fr/~stefania.dumbrava/OReilly_Graph_Databases.pdf
  • 2 https://www.quackit.com/neo4j/tutorial/

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Data Science), item 6.12 (N), under CBCS REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording, module numbering and hours are as printed in that syllabus.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

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