Jun 23, 2021 · Since people tend to cluster with others similar to them, we can use Community Detection to identify users with a high number of degrees (connections) and see how far their reach can travel in the network.

Jun 17, 2022 · The most popular community detection algorithm in the space, the Louvain algorithm is based on the idea of graph (component) density i.e. something related to edges/connections frequency...

Identifying the community sub structures within a network can provide insight into how network function and topology affect each other. vertex within a community through the vertices that are also part of the same community.

Download scientific diagram | Flow chart of the community detection method based on the Louvain algorithm. from publication: A Novel Emerging Topic Identification and Evolution Discovery...

Jan 16, 2025 · – Community detection algorithms are used to detect terrorist groups in social networks by analyzing their connections and interactions. – Using the same techniques as above, groups of malicious bots can be detected to find their origin. – Community detection can also be used to study the more vulnerable groups to a disease.

In this paper, we propose a novel Stacked Autoencoder-based deep learning approach augmented by the Crow Search Algorithm (CSA)-based k-means clustering algorithm to uncover community...

This module employs the Louvain method for community detection based on Blondel’s paper Fast unfolding of communities in large networks using the Grappolo parallel approach. Louvain’s algorithm is from the modularity maximization community detection family.

The proposed community detection algorithm flowchart. The Internet of things (IoT) and social networks integrate into a new area called the social Internet of things (SIoT). The SIoT is...

May 29, 2021 · We propose an efficient data transmission strategy for community detection (EDCD) algorithm. When dividing communities, we use mobile edge computing to combine network topology attributes with social attributes. When forwarding the message, we select optimal relay node as transmission according to the coefficients of channels.

Performs dynamic community detection using the LabelRankT algorithm. The default values of the similarity_threshold, exponent and min_value parameters are not universally applicable, and the actual values should be determined experimentally. This is especially pertinent to setting the min_value parameter.