Web3, one of the novel paradigms which may drive the evolution of the future Web, is offering an invaluable volume of data stored in the supporting blockchains. Researchers from different fields such as network science, computational social science and data mining, might benefit from these large collections of temporal and heterogeneous data capturing different kinds of interaction among people and between people and the platforms. In this study we focus on a specific issue related to these modern techno-social systems, i.e. the understanding of the rules driving their growth. To reach this goal, we performed an analysis based on graph evolution rules - GERs - on different networks gathered from Web3 platforms such as Steemit or OpenSea. Graph evolution rules mining is a frequency-based method for evaluating network evolution which does not require any prior growth process for disentangling how networks evolve. By comparing the evolution rules of social network platforms and asset trading services through GER profiles, we observe that some evolution rules are common to all Web3 platforms, regardless of the system specificity. On the other hand, in specific cases, the frequency of graph evolution rules is influenced by the nature of the platform: whereas social and token-transfer networks are characterized by rules which increase network transitivity and reciprocity, NFT trading networks, especially those specialized in a specific type of digital asset, are driven by rules which form trading chains. These findings suggest that the GER approach and the GER profiles are a good starting point to get insights into the evolutionary behavior of a network and to define a classification of graph evolution rules.

Disentangling the Growth of Blockchain-based Networks by Graph Evolution Rule Mining / A. Galdeman, M. Zignani, S. Gaito - In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) / [a cura di] J.Z. Huang,Y. Pan, B. Hammer, M.K. Khan, X. Xie, L. Cui, Y. He. - [s.l] : IEEE, 2022 Oct. - ISBN 978-1-6654-7330-9. - pp. 1-10 (( Intervento presentato al 9. convegno IEEE International Conference on Data Science and Advanced Analytics tenutosi a Shenzhen nel 2022 [10.1109/DSAA54385.2022.10032398].

Disentangling the Growth of Blockchain-based Networks by Graph Evolution Rule Mining

A. Galdeman
Primo
;
M. Zignani
Secondo
;
S. Gaito
Ultimo
2022

Abstract

Web3, one of the novel paradigms which may drive the evolution of the future Web, is offering an invaluable volume of data stored in the supporting blockchains. Researchers from different fields such as network science, computational social science and data mining, might benefit from these large collections of temporal and heterogeneous data capturing different kinds of interaction among people and between people and the platforms. In this study we focus on a specific issue related to these modern techno-social systems, i.e. the understanding of the rules driving their growth. To reach this goal, we performed an analysis based on graph evolution rules - GERs - on different networks gathered from Web3 platforms such as Steemit or OpenSea. Graph evolution rules mining is a frequency-based method for evaluating network evolution which does not require any prior growth process for disentangling how networks evolve. By comparing the evolution rules of social network platforms and asset trading services through GER profiles, we observe that some evolution rules are common to all Web3 platforms, regardless of the system specificity. On the other hand, in specific cases, the frequency of graph evolution rules is influenced by the nature of the platform: whereas social and token-transfer networks are characterized by rules which increase network transitivity and reciprocity, NFT trading networks, especially those specialized in a specific type of digital asset, are driven by rules which form trading chains. These findings suggest that the GER approach and the GER profiles are a good starting point to get insights into the evolutionary behavior of a network and to define a classification of graph evolution rules.
blockchain-based platforms; evolution profile; graph evolution mining; graph evolution rule; NFTs; subgraph mining
Settore INF/01 - Informatica
ott-2022
IEEE
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
GERatDSAA22-postreview.pdf

accesso riservato

Tipologia: Pre-print (manoscritto inviato all'editore)
Dimensione 1.82 MB
Formato Adobe PDF
1.82 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/956571
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 0
social impact