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

Related tags

Deep Learning community-detection collaborative-filtering recommendation graph-mining spectral-clustering modularity-maximization
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: image image
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Owner
Jiayi Chen
I am a Ph.D. candidate in Computer Science at University of Virginia. My research interests are machine learning and data mining.
Jiayi Chen
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