An Efficient Bitcoin Network Topology Discovery Algorithm for Dynamic Display

The Bitcoin network is made up of a huge number of nodes, which makes it difficult and time-consuming for users to figure out how everything is connected. To make this easier, we’ve come up with a new algorithm that uses lightweight “probe” nodes to quickly gather information about the network. On top of that, we’ve built a clustering system that groups stable nodes together, so we can map out the network faster and more efficiently. We’ve also created a flexible, real-time visualization tool that shows how the network is structured in layers. Our tests show that this approach cuts down communication overhead by about 72%, while still maintaining 95% accuracy in mapping the network. Even better, the same method can be used for other blockchain networks with similar setups.

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Publication Details

Journal
International Research Journal on Advanced Science Hub
Published
2025-08-26
DOI
https://doi.org/10.47392/irjash.2025.082
Citations
1
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
1.28
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article

An Efficient Bitcoin Network Topology Discovery Algorithm for Dynamic Display

Shruti Shetti, Sinchana Dsa, Manjunath Mudda
1 citations
International Research Journal on Advanced Science Hub
Visual Attention and Saliency Detection
1.28
article

An Efficient Bitcoin Network Topology Discovery Algorithm for Dynamic Display

Shruti Shetti, Sinchana Dsa, Manjunath Mudda
article en
1 citations

Abstract

The Bitcoin network is made up of a huge number of nodes, which makes it difficult and time-consuming for users to figure out how everything is connected. To make this easier, we’ve come up with a new algorithm that uses lightweight “probe” nodes to quickly gather information about the network. On top of that, we’ve built a clustering system that groups stable nodes together, so we can map out the network faster and more efficiently. We’ve also created a flexible, real-time visualization tool that shows how the network is structured in layers. Our tests show that this approach cuts down communication overhead by about 72%, while still maintaining 95% accuracy in mapping the network. Even better, the same method can be used for other blockchain networks with similar setups.

International Research Journal on Advanced Science HubVol. 7(08)
Seva Mandir (IN)
Openalex Percentile: Top 16%
Visual Attention and Saliency Detection
1.28
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