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B.E Artificial Intelligence and Machine Learning Graph Data Science Syllabus - Mumbai University 2026

This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Graph-Data-Science.pdf
Semester 8 · Fourth Year AI-ML

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Syllabus for Graph Data Science

Semester 8 · Fourth Year AI-ML

Module 1: Introduction to Graph

  • Definitions and examples, Three puzzles, Paths and cycles, Connectivity, Eulerian graphs, Hamiltonian graphs, shortest path, Chinese postman problem, traveling salesman problem, trees, properties of trees

Module 2: Introduction Graph databases

  • A High-Level View of the Graph Space, Graph Databases, Graph Compute Engines, The Power of Graph Databases, Performance, Flexibility, Agility, Options for Storing Connected Data, Relational Databases Lack Relationships, NOSQL Databases Also Lack Relationships, Graph databases embraces relationship

Module 3: Data Modelling with Graphs

  • Models and Goals, The Labelled Property Graph Mode Querying Graphs, A Comparison of Relational and Graph Modelling, Cross-Domain Models, Common Modelling Pitfalls, Identifying Nodes and Relationships, Avoiding Anti-Patterns

Module 4: Building a Graph Database Application

  • Data Modelling , Application Architecture ,Testing ,Capacity Planning ,Importing and Bulk Loading Data ,

Module 5: Graphs in the Real World

  • Organizations Choose Graph Databases, Common Use Cases, Real-World Examples, Authorization and Acces Control, Geospatial and Logistics, Graph Database Internals, Native Graph Processing, Native Graph Storage Programmatic APIs, Kernel API, Core API, Traversa Framework, Non-functional Characteristics

Module 6: case study

  • Neo4j – About, Neo4j – Installation, Neo4j – Browser Neo4j - Query Language (Cypher), Neo4j - Create a Node Neo4j - Create a Relationship, Neo4j - Create an Index Neo4j - Create a Constraint, Neo4j - Select Data with MATCH, Neo4j - Import Data from CSV, Neo4j - Drop an Index, Neo4j - Drop a Constraint, Neo4j - Delete a Node, Neo4j - Delete a Relationship

Textbooks

  • 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 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 References:
  • 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 Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.

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.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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