Research Topic

Blockchain Through the Lens of Network Science

About this Research Topic

Blockchain as a trusted transaction register is being adopted as a ledger in multiple application areas. Initially started as a platform for cryptocurrencies, its applications are nowadays widespread and cover supply chains, financial services, healthcare, and public registers to name a few. As entities in a blockchain are linked through transactions, blockchain is a complex network. In such a network of transactions, first-order approaches in understanding blockchain are not enough to capture its complexity. Network science has developed multiple methods that allow for higher-order analysis of the networks. The apparatus of methods coming from this scientific discipline has already left a large footprint in other types of complex systems (including financial, social, and biological) and has only recently started to be applied to blockchain. Network science is capable of bringing new insights into the analysis of blockchain systems, in order to boost their performance and increase their security as well as other important factors for the smooth evolution of blockchain-based systems.

In order to tackle the challenges found in complex networks, network science is linking multiple disciplines such as physics, computer science, mathematics, and other domain-dependent disciplines such as sociology, biology, or chemistry. The goal of this Research Topic is to provide the scientific community with the advancements of network science applied to blockchain by bridging them together. We intend to present the state of the art in the area of network science methods applied to blockchain-based solutions. Network science has already boosted other research disciplines and industries in the past; we now expect new knowledge that can be generated and used for improving blockchain. In this Research Topic, we intend to gather research manuscripts on tools, techniques, and results that are coming from network science and that are applied to blockchain.

We are interested in manuscripts that cover the following, or related areas:
• Network methods for modeling and analyzing blockchain transaction graphs;
• Tools and techniques coming from network science that facilitate research on blockchain-based systems;
• Combining machine learning and network science in blockchain;
• Improving the security of blockchain solutions;
• Improving the robustness and functionality of blockchain;
• Predicting the evolution of blockchain;
• Introducing novel deanonymization techniques.

We are not limiting the application areas of the research; the blockchain-based systems can belong to any of the sectors: cryptocurrencies, healthcare, finance, supply chain, internet of things, voting, etc. The common denominator being network science used as a research methodology.

Topic Editor Panagiotis Karampourniotis is employed by the company Aetna. All other Topic Editors declare no competing interests with regards to the Research Topic subject.


Keywords: Blockchain, Network Science, Complex Networks, Machine Learning, Security


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Blockchain as a trusted transaction register is being adopted as a ledger in multiple application areas. Initially started as a platform for cryptocurrencies, its applications are nowadays widespread and cover supply chains, financial services, healthcare, and public registers to name a few. As entities in a blockchain are linked through transactions, blockchain is a complex network. In such a network of transactions, first-order approaches in understanding blockchain are not enough to capture its complexity. Network science has developed multiple methods that allow for higher-order analysis of the networks. The apparatus of methods coming from this scientific discipline has already left a large footprint in other types of complex systems (including financial, social, and biological) and has only recently started to be applied to blockchain. Network science is capable of bringing new insights into the analysis of blockchain systems, in order to boost their performance and increase their security as well as other important factors for the smooth evolution of blockchain-based systems.

In order to tackle the challenges found in complex networks, network science is linking multiple disciplines such as physics, computer science, mathematics, and other domain-dependent disciplines such as sociology, biology, or chemistry. The goal of this Research Topic is to provide the scientific community with the advancements of network science applied to blockchain by bridging them together. We intend to present the state of the art in the area of network science methods applied to blockchain-based solutions. Network science has already boosted other research disciplines and industries in the past; we now expect new knowledge that can be generated and used for improving blockchain. In this Research Topic, we intend to gather research manuscripts on tools, techniques, and results that are coming from network science and that are applied to blockchain.

We are interested in manuscripts that cover the following, or related areas:
• Network methods for modeling and analyzing blockchain transaction graphs;
• Tools and techniques coming from network science that facilitate research on blockchain-based systems;
• Combining machine learning and network science in blockchain;
• Improving the security of blockchain solutions;
• Improving the robustness and functionality of blockchain;
• Predicting the evolution of blockchain;
• Introducing novel deanonymization techniques.

We are not limiting the application areas of the research; the blockchain-based systems can belong to any of the sectors: cryptocurrencies, healthcare, finance, supply chain, internet of things, voting, etc. The common denominator being network science used as a research methodology.

Topic Editor Panagiotis Karampourniotis is employed by the company Aetna. All other Topic Editors declare no competing interests with regards to the Research Topic subject.


Keywords: Blockchain, Network Science, Complex Networks, Machine Learning, Security


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

15 January 2021 Manuscript
02 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

15 January 2021 Manuscript
02 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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