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Computer Science
Big Data
Apache Spark
1. Introduction to Apache Spark
2. Core Spark Concepts
3. Spark Architecture and Execution
4. Spark SQL and Structured APIs
5. Structured Streaming
6. Machine Learning with MLlib
7. Graph Processing with GraphX
8. Performance Tuning and Optimization
Graph Processing with GraphX
GraphX Fundamentals
Graph Processing Use Cases
Social Network Analysis
Recommendation Systems
Fraud Detection
Network Analysis
GraphX vs Other Frameworks
Neo4j Comparison
Apache Giraph Comparison
NetworkX Integration
Property Graph Model
Graph Structure
Vertex Representation
Vertex IDs
Vertex Properties
Edge Representation
Edge Properties
Directed vs Undirected
Triplet Abstraction
Source-Edge-Destination
Property Access
Graph Construction
From RDDs
From External Sources
Graph Builders
Graph Operations
Structural Operations
Graph Transformation
subgraph Operations
reverse Operations
Vertex Operations
mapVertices
mapTriplets
Edge Operations
mapEdges
groupEdges
Join Operations
Vertex Joins
joinVertices
outerJoinVertices
Edge Joins
joinEdges
outerJoinEdges
Aggregation Operations
aggregateMessages
collectNeighbors
degrees Operations
Graph Algorithms
Centrality Algorithms
PageRank
Static PageRank
Dynamic PageRank
Degree Centrality
Betweenness Centrality
Connectivity Algorithms
Connected Components
Weakly Connected
Strongly Connected
Triangle Counting
Clustering Coefficient
Path Algorithms
Shortest Paths
Single Source
All Pairs
Breadth-First Search
Depth-First Search
Community Detection
Label Propagation
Modularity Optimization
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8. Performance Tuning and Optimization