Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Last update: Mar 03, 2022

Overview

Graph Mining

Author: Jiayi Chen

Time: April 2021

Implemented Algorithms:

Network:
- Scrabing Data, Network Construbtion and Network Measurement (e.g., Pagerank, clustering coefficient, ...)
Recommendation:
- User-based Collaborative Filtering
- Item-based Collaborative Filtering
Community Detection:
- Spectral clustering algorithm
- Modularity maximization algorithm

Requirements

python 3
networkx
pandas

Getting Started

Scrabing Data & Network Construbtion & Measurement

run "/graph_scraping_construction_measurement/main.py"

Collaborative Filtering

run "/collaborative_filtering/main.py"

User-based collaborative filtering: "/collaborative_filtering/user_based_CF.py"
Item-based collaborative filtering: "/collaborative_filtering/item_based_CF.py"

Community Detection

Community number is set to k=2.

run "/community_detection/main.py"

Spectral clustering: /community_detection/spectral_clustering.py
modularity maximization: /community_detection/modularity_maximization.py
Result example:

Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Related tags

Overview

Graph Mining

Implemented Algorithms:

Requirements

Getting Started

Scrabing Data & Network Construbtion & Measurement

Collaborative Filtering

Community Detection

Owner

Jiayi Chen

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