A Community Detection-Based Blockchain Sharding Scheme

Zhang, Z; Wang, X; Yu, G; Ni, W; Liu, RP; Georgalas, N; Reeves, A

A Community Detection-Based Blockchain Sharding Scheme

Zhang, Z Wang, X

Yu, G Ni, W Liu, RP Georgalas, N Reeves, A

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13733 LNCS, pp. 78-91
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Z
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437
dc.contributor.author	Yu, G
dc.contributor.author	Ni, W
dc.contributor.author	Liu, RP
dc.contributor.author	Georgalas, N
dc.contributor.author	Reeves, A
dc.date.accessioned	2023-04-13T03:52:22Z
dc.date.available	2023-04-13T03:52:22Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13733 LNCS, pp. 78-91
dc.identifier.isbn	9783031234941
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/169752
dc.description.abstract	Sharding has been considered a promising approach to improving blockchain scalability. However, multiple shards result in a large number of cross-shard transactions, which require a long confirmation time across shards and thus restrain the scalability of sharded blockchains. In this paper, we convert the blockchain sharding challenge into a graph partitioning problem on undirected and weighted transaction graphs that capture transaction frequency between blockchain addresses. We propose a new sharding scheme using the community detection algorithm, where blockchain nodes in the same community frequently trade with each other. The detected communities are used as shards for node allocation. The proposed community detection-based sharding scheme is validated using public Ethereum transactions over one million blocks. The proposed community detection-based sharding scheme is able to reduce the ratio of cross-shard transactions from 80% to 20%, as compared to baseline random sharding schemes, and retain the ratio of around 20% over the examined one million blocks.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-031-23495-8_6
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A Community Detection-Based Blockchain Sharding Scheme
dc.type	Conference Proceeding
utslib.citation.volume	13733 LNCS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-12-25T00:00:00+1000Z
dc.date.updated	2023-04-13T03:52:20Z
pubs.publication-status	Published
pubs.volume	13733 LNCS

Abstract:

Sharding has been considered a promising approach to improving blockchain scalability. However, multiple shards result in a large number of cross-shard transactions, which require a long confirmation time across shards and thus restrain the scalability of sharded blockchains. In this paper, we convert the blockchain sharding challenge into a graph partitioning problem on undirected and weighted transaction graphs that capture transaction frequency between blockchain addresses. We propose a new sharding scheme using the community detection algorithm, where blockchain nodes in the same community frequently trade with each other. The detected communities are used as shards for node allocation. The proposed community detection-based sharding scheme is validated using public Ethereum transactions over one million blocks. The proposed community detection-based sharding scheme is able to reduce the ratio of cross-shard transactions from 80% to 20%, as compared to baseline random sharding schemes, and retain the ratio of around 20% over the examined one million blocks.

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http://hdl.handle.net/10453/169752