Data Rate and External Lighting Variations for Bit Error Rate Measurement on A Simple Single LED Visible Light Communication System

Wicaksono, None Khalfan Nadhief Prayoga; Apriono, Catur

doi:10.5109/6625734

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…are interesting and complicated system 1,2,3) . These systems are made up of nodes and links forming networks 4,5,6) . Smallworld phenomenon 7) , power-law degree distribution, and the presence of community structure 8,9,10) where nodes cluster normally into closely linked modules, often called communities, with only the barest of ties between them, have all been discovered through research in a variety of academic domains.…”

Section: Introductionmentioning

confidence: 99%

Envisaging Modularity Detecting Communities in Networks: Gephi Visuals

Gautam,

Gupta

2023

Evergreen

View full text Add to dashboard Cite

Understanding the intricate connections within networks relies on exploring their connection patterns and extracting valuable statistics about their overall framework. Among the various aspects of complex networks, community structure plays a crucial role in contemporary times. Detecting and analyzing these communities is paramount in applications such as information sharing, dissemination, recommendation systems, and classification. Modularity optimization presents a formidable approach to discerning both the community structure within a complex network and the internal structure of its nodes. This paper aims to contribute to the identification and visualization of communities within networks, highlighting their distinctive attributes that set them apart from the typical network structure. By employing graph theoretical analysis, our study utilizes the Gephi software to detect and represent communities through their visuals with the aid of modularity and also provides key statistical insights into the network. It explores the application of Gephi, a graph visualization software, to visually represent these communities, providing insights that extend beyond traditional network analysis. Additionally, we present a comparison between modularity and the clustering coefficient, shedding further light on the network's characteristics. By using Gephi visualization capabilities, we present a novel approach to gain deeper insights into network structures, thus contributing to a more profound understanding of complex networks and their community dynamics.

show abstract