https://github.com/timkong21/memgraph-graph-analytics-with-networkx
Graph Analytics delivered by MEMGRAPH academy
https://github.com/timkong21/memgraph-graph-analytics-with-networkx
analytics breadth-first-search c community-detection depth-first-search ford-fulkerson girvan-newman graph k-means-clustering link-analysis link-prediction louvain-community-detection machine-learning networkx node-classification page-rank pathfinding python
Last synced: 2 months ago
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Graph Analytics delivered by MEMGRAPH academy
- Host: GitHub
- URL: https://github.com/timkong21/memgraph-graph-analytics-with-networkx
- Owner: TimKong21
- Created: 2021-07-29T04:10:01.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-02-02T20:28:44.000Z (over 2 years ago)
- Last Synced: 2025-02-06T07:21:22.778Z (4 months ago)
- Topics: analytics, breadth-first-search, c, community-detection, depth-first-search, ford-fulkerson, girvan-newman, graph, k-means-clustering, link-analysis, link-prediction, louvain-community-detection, machine-learning, networkx, node-classification, page-rank, pathfinding, python
- Language: Jupyter Notebook
- Homepage:
- Size: 20.1 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Graph-Analytics-with-NetworkX
## About this course
Graph analytics and graph databases are one of the fastest-growing areas in data analytics and machine learning. Companies like Google, UberEats, Pinterest and Twitter, have leveraged graphs to transform their core products. As more enterprises embrace graphs, there is a huge demand for engineers and data scientists with graph analytics skills.
During this [course](https://github.com/memgraph/memgraph-academy), you will learn everything you need to know to build an application using Python graph algorithm libraries, visualization tools, and graph databases. You will start with basic graph concepts, work your way to graph algorithms, and finish the course by building a graph-based fraud detection application from scratch.
Here is the list of the basic requirements for the course:
:small_blue_diamond: Python 3.x - preferably 3.9
:small_blue_diamond: NetworkX and Matplotlib packages
:small_blue_diamond: A code editor - we recommend Visual Studio Code (VSC)
:small_blue_diamond: A GitHub account