complex networks
Recently Published Documents





2022 ◽  
Vol 155 ◽  
pp. 111703
Pitambar Khanra ◽  
Subrata Ghosh ◽  
Karin Alfaro-Bittner ◽  
Prosenjit Kundu ◽  
Stefano Boccaletti ◽  

2022 ◽  
Vol 9 ◽  
Wenbo Song ◽  
Wei Sheng ◽  
Dong Li ◽  
Chong Wu ◽  
Jun Ma

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 241
Alberto Partida ◽  
Saki Gerassis ◽  
Regino Criado ◽  
Miguel Romance ◽  
Eduardo Giráldez ◽  

In this article, we model the two most market-capitalised public, open and permissionless blockchain implementations, Bitcoin (BTC) and Ethereum (ETH), as a System of Systems (SoS) of public blockchains. We study the concepts of blockchain, BTC, ETH, complex networks, SoS Engineering and intentional risk. We analyse BTC and ETH from an open SoS perspective through the main properties that seminal System of Systems Engineering (SoSE) references propose. This article demonstrates that these public blockchain implementations create networks that grow in complexity and connect with each other. We propose a methodology based on a complexity management lever such as SoSE to better understand public blockchains such as BTC and ETH and manage their evolution. Our ultimate objective is to improve the resilience of public blockchains against intentional risk: a key requirement for their mass adoption. We conclude with specific measures, based on this novel systems engineering approach, to effectively improve the resilience against intentional risk of the open SoS of public blockchains, composed of a non-inflationary money system, “sound money”, such as BTC, and of a world financial computer system, “a financial conduit”, such as ETH. The goal of this paper is to formulate a SoS that transfers digital value and aspires to position itself as a distributed alternative to the fiat currency-based financial system.

2022 ◽  
S. Akhtanov ◽  
D. Turlykozhayeva ◽  
N. Ussipov ◽  
M. Ibraimov ◽  
Z. Zhanabaev

2022 ◽  
pp. S119-S144
Leonardo Stella ◽  
Alejandro Pinel Martínez ◽  
Dario Bauso ◽  
Patrizio Colaneri

2022 ◽  
Vol 7 (1) ◽  
Samir Chowdhury ◽  
Steve Huntsman ◽  
Matvey Yutin

AbstractPath homology is a powerful method for attaching algebraic invariants to digraphs. While there have been growing theoretical developments on the algebro-topological framework surrounding path homology, bona fide applications to the study of complex networks have remained stagnant. We address this gap by presenting an algorithm for path homology that combines efficient pruning and indexing techniques and using it to topologically analyze a variety of real-world complex temporal networks. A crucial step in our analysis is the complete characterization of path homologies of certain families of small digraphs that appear as subgraphs in these complex networks. These families include all digraphs, directed acyclic graphs, and undirected graphs up to certain numbers of vertices, as well as some specially constructed cases. Using information from this analysis, we identify small digraphs contributing to path homology in dimension two for three temporal networks in an aggregated representation and relate these digraphs to network behavior. We then investigate alternative temporal network representations and identify complementary subgraphs as well as behavior that is preserved across representations. We conclude that path homology provides insight into temporal network structure, and in turn, emergent structures in temporal networks provide us with new subgraphs having interesting path homology.

Sign in / Sign up

Export Citation Format

Share Document