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Graph Mining: An Introduction
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Our Courses
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Career Track-based Courses
Big Data, Machine Learning, and Data Science
Cloud Administration and Engineering
System Administration in Linux and Unix
CMS - Content Management System
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Full Stack Web Development
Project and Program Management
Mobile Application Development
Front End Developer
Software Testing
Communications
Java, Java EE
C# - C Sharp
Miscellaneous Videos
Active-Programs
Bronze: Data Science, Analytics, AI, and Machine Learning
Silver: Data Science, Analytics, AI, and Machine Learning
Gold: Data Science, Analytics, AI, and Machine Learning
Platinum: Data Science, Analytics, AI, and Machine Learning
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পাঠ্যক্রমসমূহ
Graph Mining: An Introduction
Big Data Graph Processing
Graph Processing with Hadoop
URL টি খুলতে
https://analyticsindiamag.com/how-graph-processing-gets-a-makeover-with-hadoop/
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Misc Announcements
Prerequisite
Job Prospect for Graph Mining
Introducing Graph Mining Course
Course Topics
Resources: Public URLs for Some of the Included Topics
Everyday 20:00 PM EST (Toronto): 6 AM (Dhaka) Chat with each other to discuss the course topics
Everyday at 10:00 AM EST (Toronto), 8 PM (Bangladesh): Chat with Others
Discussion Forum: Discuss Course Topics By Modules
Learning Outcomes
Assessments when a full length course
Assessment for a Certificate course: Short Term: An Introductory Course
Resources to Learn From
Read the resources above on Introducing Graphs. Then Learn by finding answers to the questions in this list?
Quiz : Pre Test (i.e. Start of the class)
Quiz : Post Test : End of the class
Every Class Assignment: Reflection on today's class
Resources to Learn From
Read the resources above and find answer on SMALL WORLD GRAPHS & RANDOM GRAPH GENERATORS. Learn by finding answers to the following questions?
Resources to Learn From
Read the resources above and Learn by finding Answers to the Following Questions. Can you answer the following questions?
Resources to Learn From
Read the resources above to find answers. Betweenness Based Clustering: Learn by finding answers to the following questions. Can you answer the following?
Resources to Learn From
Read the resources above to find answers. Community Detection: Learn by finding answers to the following questions. Can you answer the following questions on Community Detection?
Resources to Learn From
Read the resources above to find answers. Shared Nearest Neighbors : Clustering : Community Detection
Resources to Learn From
Read the resources above to find answers. K-Spanning Trees: Learn by finding answers to the following questions. Can you answer the questions?
Resources to Learn From
Read the resources above to find answers. Louvian Modularity: Learn by finding answers to the following questions. Can you answer the following questions?
Resources to Learn From
Read the resources above to find answers. Highly Connected Subgraph Clustering: Learn by finding answers to the following questions.
Resources to Learn From
Read the resources above to find answers. Link Prediction. Learn by finding answers to the following questions.
To Learn From: Time Evolving Graphs
Learn by finding the answers to the following questions from the given/external resources?
To Learn From: Graphs are everywhere
Learn From: Anomaly Detection and Graph Mining
Graph based Anomaly Detection and Description: A Survey
Resources to learn from
A presentation on Influence/Virus Propagation
Resources to Learn From
Graph Database
Neo4j
Resources to Learn From
Tool
Techniques, Tools and Applications of Graph Analytic
Graph Processing: GraphX, GraphFrames
Learn From: Social Network Analysis
Learn From: Querying Graphs: Isomorphic Graphs
Learn From: Mining graph patterns
Resources to Learn From
Resources to Learn From: All topics below
Graph Classifications: Resources to Learn From
Graph Kernel (Based Classifications) : Resources
List 1: Top 30 Social Network Analysis and Visualization Tools
List 2: Graph Analytics Tools
Graph Datasets for Assignments and Projects; also for research
Python or R Libraries for Graph Mining Implementations
List 1: Implement the following algorithms and apply on large graphs (in Plain Python or R without Graph Libraries)
List 2: Use Graph Algorithms provided by NetworkX (Python, R) libraries
List 3: Spanning Tree, Connected Subgraphs
List 4: Based on Graph Properties and Concepts
List 5: Centrality-PageRank-Betweenness
List 1: Project Ideas with real life applications
List 2: Check the Data Science Competitions to get Project Ideas
List 3: Take a Google Function Area in Graph Mining and Apply on Very Large Graphs and see what insight you can get
List 1: Research on Google Job/Function areas in Graph Mining
My Implementation: Louvian Algorithm for Community Detection in Large Graph Networks
GraphX Implementation of Louvian Modularity Algorithm for Community Detection
PageRank Algorithm Implementation
Centrality and Betweenness or similar concept implementation.
Dijkstra’s shortest path algorithm
Floyd Warshall Algorithm | All pair Shortest Path
Karger's mincut algorithm in Python