community-detection-algorithms
There are 1 repository under community-detection-algorithms topic.
- Language:C++ 192
PanShi2016 / Community_Detection
Baseline Algorithms for Community Detection
Language:C++ 12Shimin-CUFE / KMLPA
A Python implementation of improved Label Propagation Algorithm.
Language:Python 10CarlosVargasF / Community_Discovery_algorithm_comparison
Code and results of internship work performed by Carlos Vargas under supervision of Phd Rémy Cazabet, at LIRIS (https://liris.cnrs.fr/).
Language:Jupyter Notebook 8marcottelab / super.complex
Super.Complex is a supervised machine learning algorithm for community detection in networks. It learns information from known communities and uses this information to find new communities on the network.
Language:Python 3standardxnonconformist / community-detection-algorithms-comparision-R
A summary of the work done for the June 2021 FOSSEE Research Internship in R.
Language:R 3volkantunali / SimCMR
Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement
Language:Python 3Akshay-A-Kulkarni / Twitter-Graph-Clustering
Community detection algorithms on custom twitter hashtag networks
Language:Vue 2KuroginQin / ICD
[TKDD'23] Demo code of the paper entitled "Towards a Better Trade-Off between Quality and Efficiency of Community Detection: An Inductive Embedding Method across Graphs", which has been accepted by ACM TKDD
Language:Python 2SeifTarekNassar / Community-Detection-Algorithms-Performance-comparison
The implementation was done using python networkx and matplot libraries. Zachary karate club dataset is used as a benchmark dataset between the different community detection algorithms. Karate is the well-known and much-used dataset to benchmark algorithms as ground truth of it is two so if a detection algorithm is close to these two sets, then it is most likely to be accurate at detection. The data was collected from the members of a university karate club by Wayne Zachary in 1977. Each node represents a member of the club, and each edge represents a tie between two members of the club. The network is undirected. The network captures 34 members of a karate club, documenting links between pairs of members who interacted outside the club. During the study a conflict arose between the administrator "John A" and instructor "Mr. Hi", which led to the split of the club into two. Half of the members formed a new club around Mr. Hi; members from the other part found a new instructor or gave up karate. Based on collected data Zachary correctly assigned all but one member of the club to the groups they joined after the split.
Language:Python 2LorenzoPolli / local-community-detection
This repository includes my project in Python for the Advanced Software Praktika in Data and Text Mining at the University of Heidelberg
Language:Jupyter Notebook 1mokeddembillel / Community-Finder
an Algorithm that works on Community Detection
Language:C#Betweenness centrality method on WormNetv3 Network
Language:Python
