A Python implementation of improved Label Propagation Algorithm.
Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement
A Python project for analyzing network structures and detecting communities using various algorithms such as Louvain, Leiden, Infomap, and others. The project supports both directed and undirected graphs, computes network statistics, detects communities, and exports comparative evaluation results.
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.
[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
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 …
Betweenness centrality method on WormNetv3 Network
Debiasing users and items in Collaborative Filtering Recommender Systems via Community Edge Suppression
A FluidC analysis on large-scale and real-data complex networks.
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