Graphs Processing & Algorithms
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Follow on Instagram for UpdatesGraphs Processing & Algorithms Syllabus — Unit-wise Content
The official GTU curriculum for Graphs Processing & Algorithms (subject code ME02095041) as prescribed for AIAD · Semester 2 divides the course into 7 units, covering 45 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02095041)Unit 1 Unit-I
- Introduction to graphs What Are Graphs? What Are Graph Analytics and Algorithms? Graph Processing
- Queries, and Algorithms
- Graph Types and Structures
- Scale-Free Structures
- OLTP and OLAP
- Why Should We Care About Graph Algorithms?
- Graph Analytics Use Cases
Unit 2 Unit-II
- Graph Platforms and Processing Platform Considerations
- Processing Considerations
- Representative Platforms-Selecting Our Platform
- Apache Spark
- Neo4j Graph Platform
Unit 3 Unit-III
- Path Finding and Graph Search Algorithms Importing the Data into Apache Spark
- Importing the Data into Neo4j
- Breadth First Search with Apache Spark
- Shortest Path
- Shortest Path (Weighted) with Apache Spark
- All Pairs Shortest Path
- Single Source Shortest Path
- Minimum Spanning Tree
- Random Walk
Unit 4 Unit-IV
- Centrality Algorithms Example Graph Data: The Social Graph
- Degree Centrality
- Closeness Centrality
- Betweenness Centrality
- Program Name: Master of Engineering Level: PG Branch: Artificial Intelligence and Data Science Subject Code: ME02095041 Course/Subject Name: Graphs Processing & Algorithms
Unit 5 Unit-V
- Community Detection Algorithms Example Graph Data: The Software Dependency Graph
- Triangle Count and Clustering Coefficient
- Strongly Connected Components
- Connected Components
- Label Propagation
- Louvain Modularity
Unit 6 Unit-IV
- Graph Algorithms in Practice Analyzing Yelp Data with Neo4j
- Analyzing Airline Flight Data with Apache Spark
- Graphs and Machine Learning in Practice: Link Prediction- Tools and Data
- Importing the Data into Neo4j
- The Coauthorship Graph
- Creating Balanced Training and Testing Datasets
- How We Predict Missing Links
- Creating a Machine Learning Pipeline
- Predicting Links: Basic Graph Features
- Predicting Links: Triangles and the Clustering Coefficient
- Predicting Links: Community Detection
Unit 7 Unit-VII
- Current Trends The latest technology and algorithms in real time applications like Netflix
- Gmail with web analytics
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