Cooperative stalking of transient nomadic resources on overlay networks

Exarchakos, Georgios; Antonopoulos, Nick

doi:10.1016/j.future.2012.12.008

Cited by 3 publications

(2 citation statements)

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“…Our homogeneous artificial system has two virtual elements: a group of wandering thieves (in game theory: the actors) and a set of virtual diamonds or vdiamonds (in game theory: the resources). At start, each node is artificially endowed with vdiamonds which are nomadic, reusable and non-replicable virtual resources, generalizing and virtualizing the concept from 12 , 24 . Likewise, each node is endowed with wandering thieves, mobile actors which act stochastic (they wander in search of vdiamonds to steal) and egoistic (as soon as they have an opportunity, they steal vdiamonds and take them back to their home node).…”

Section: Resultsmentioning

confidence: 99%

Decentralized dynamic understanding of hidden relations in complex networks

Mocanu

Exarchakos

Liotta

2018

Sci Rep

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Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks.

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Section: Resultsmentioning

confidence: 99%

Decentralized dynamic understanding of hidden relations in complex networks

Mocanu

Exarchakos

Liotta

2018

Sci Rep

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“…Trail decay mechanisms slow down the unlearning process in case of fast changes in the network. Based on this observation, DIP [14] and Stalkers [15] have proposed decentralized foraging algorithms relying on the previous explorations. Stalkers have used nomadic reusable non-replicable resources which migrate from node to node when released and requested.…”

Section: Swarm Intelligencementioning

confidence: 99%

Node centrality awareness via swarming effects

Mocanu

Exarchakos

Liotta

2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Centralization is a weakness in large scale dynamic topologies and, thus, collaboratively electing at runtime the most impactful (central) nodes is necessary to ensure reliability. However, little has been achieved in measuring the centrality of nodes in an accurate, fast, decentralized and with low overhead method. This paper proposes a swarm-inspired approach (DA-NIS) to detect the nodes that would most impact the network connectivity if removed. The idea lies on the trivial fact that the more accessible a node is, the more resources per time unit it loses. Experiments on random, scale-free and small-world graph topologies indicate that DANIS achieves higher accuracy, faster convergence and fewer communication overhead compared to other methods.

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