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https://doi.org/10.1109/iisec5...
Article . 2021 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
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Evaluation of algebraic graph clustering algorithms for complex networks

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 16 Dec 2021Publisher:IEEEJournal:2021 2nd International Informatics and Software Engineering Conference (IISEC)
Authors: Kayhan Erciyes;

doi: 10.1109/iisec54230.2021.9672390

handle: 20.500.12415/7381

Evaluation of algebraic graph clustering algorithms for complex networks

Abstract

We review main graph clustering algorithms which are MST-based, Shared Nearest Neighbor and Edge-Betweenness algorithms and show novel algebraic graph implementations using Python. We compare them using randomly generated scale-free graphs and provide pointers for parallel processing

Related Organizations
Keywords

Graph Clustering, Mst-Based Clustering, Edge-Betweenness Clustering, Shared Nearest Neighbor Clustering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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