Scale-Free Networks

Barabási, Albert‐László; Bonabeau, Eric

doi:10.1038/scientificamerican0503-60

Cited by 1,886 publications

(1,193 citation statements)

References 0 publications

Supporting

Mentioning

1,126

Contrasting

Unclassified

Order By: Relevance

“…We were able to generate graphs with scaling factor within 1.5 ≤ α ≤ 4.9. This is not a problem, because researchers claim that for most real networks α falls approximately between 2 and 3 [41], [42]. Thus, we show the results for this range, without loss of generality.…”

Section: Random Walk Vs ρ-Geodesic Between-nessmentioning

confidence: 77%

“…We investigate the impact of this difference on the importance of nodes in synthetic random networks with power law degree distribution (P ∝ degree(υ i ) −α ), generated by the Havel-Hakimi [39], [40] algorithm. We chose the power law distribution because it is the most common in real networks [41]. We were able to generate graphs with scaling factor within 1.5 ≤ α ≤ 4.9.…”

Section: Random Walk Vs ρ-Geodesic Between-nessmentioning

confidence: 99%

See 1 more Smart Citation

The Power of Quasi-Shortest Paths: $\rho$ -Geodesic Betweenness Centrality

Medeiros¹,

Campista²,

Mitton³

et al. 2017

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

Section: Random Walk Vs ρ-Geodesic Between-nessmentioning

confidence: 77%

Section: Random Walk Vs ρ-Geodesic Between-nessmentioning

confidence: 99%

The Power of Quasi-Shortest Paths: $\rho$ -Geodesic Betweenness Centrality

Medeiros¹,

Campista²,

Mitton³

et al. 2017

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

“…They separately modeled networks with nearest neighbor, random, and scale free small--world organization. In scale--free networks, the number of connections from a node is distributed not in a Gaussian fashion but according to a power law so that most nodes have few connections but a few have a very large number (Barabási & Bonabeau, 2003). Within each organizational scheme they varied whether a select group of cells or a random group received inputs from light.…”

Section: Integrating Intracellular Operations Into An Intercellular Nmentioning

confidence: 99%

Mechanists Must be Holists Too! Perspectives from Circadian Biology

Bechtel

2016

J Hist Biol

View full text Add to dashboard Cite

The pursuit of mechanistic explanations in biology has produced a great deal of knowledge about the parts, operations, and organization of mechanisms taken to be responsible for biological phenomena. Holist critics have often raised important criticisms of proposed mechanistic explanations, but until recently holists have not had alternative research strategies through which to advance explanations. This paper argues both that the results of mechanistic strategies has forced mechanists to confront ways in which whole systems affect their components and that new representational and modeling strategies are providing tools for understanding these effects of whole systems upon components. Drawing from research on the mechanism responsible for circadian rhythms in mammals, I develop two examples in which mechanistic analysis is being integrated into a more holist perspective: research revealing intercellular integration of circadian mechanisms with those involved in cell metabolism and research revealing that stable rhythms are dependent on how individual cells in the suprachiasmatic nucleus synchronize with each other to generate regular rhythms. Tools such as network diagramming and computational modeling are providing means to integrate mechanistic models into accounts of whole systems.

show abstract

“…Comparison between the structure of (a) random network and (b) scale-free network. In the scale-free network, the hubs are highlighted with black nodes verified to be the scale-free network [8][9][10], whose degree distribution follows a power law, i.e.,P (κ) ∼ κ −γ , where P (κ) is the probability that a randomly selected node has κ connections, and γ is the power of the degree distribution [18][19][20]. The comparison between the structure of the random network and the structure of the scale-free network is given in Fig.…”

Section: Introductionmentioning

confidence: 99%

Recommender Searching Mechanism for Trust-Aware Recommender Systems in Internet of Things

et al. 2013

View full text Add to dashboard Cite

Original scientific paperIntelligent things are widely connected in Internet of Things (IoT) to enable ubiquitous service access. This may cause heavy service redundant. The trust-aware recommender system (TARS) is therefore proposed for IoT to help users finding reliable services. One fundamental requirement of TARS is to efficiently find as many recommenders as possible for the active users. To achieve this, existing approaches of TARS choose to search the entire trust network, which have very high computational cost. Though the trust network is the scale-free network, we show via experiments that TARS cannot find satisfactory number of recommenders by directly applying the classical searching mechanism. In this paper, we propose an efficient searching mechanism, named S_Searching: based on the scale-freeness of trust networks, choosing the global highest-degree nodes to construct a Skeleton, and searching the recommenders via this Skeleton. Benefiting from the superior outdegrees of the nodes in the Skeleton, S_Searching can find the recommenders very efficiently. Experimental results show that S_Searching can find almost the same number of recommenders as that of conducting full search, which is much more than that of applying the classical searching mechanism in the scale-free network, while the computational complexity and cost is much less.Key words: Searching Mechanism, Trust Network, Recommender System, Scale-freeness Mehanizam pretraživanja preporučitelja za sustave sigurnih preporučitelja u Internetu stvari. Inteligentni objekti su naširoko povezani u Internet stvari kako bi se omogućio sveprisutni pristup uslugama. To može imati za posljedicu veliku redundanciju usluga. Stoga je za pronalaženje pouzdane usluge u radu predložen vjerodostojan sustav preporučitelja (VSP). Temeljni zahtjev VSP-a je učinkovito pretraživanje maksimalnog mogućeg broja preporučtelja za aktivnog korisnika. Kako bi se to postiglo, postojeći pristupi VSP-a u potpunosti pretražuju sigurnu mrežu što ima za posljedicu velike računske zahtjeve. Iako je sigurna mreža mreža bez skale, eksperimentima je pokazano kako VSP ne može naći zadovoljavajući broj preporučitelja direktnom primjenom klasičnog algoritma pretraživanja. U ovom radu je predložen učinkovit algoritam pretraživanja, nazvan S_Searching: temeljen na sigurnim mrežama bez skale koji korističvorove globalno najvećeg stupnja za izgradnju Skeleton-a i pretražuje preporučitelja pomoću Skeleton-a. Iskorištavanjem nadreîenih izlaznih stupnjevačvorova Skeleton-a S_Searching može s visokom učinkovitošću pronaći preporučitelje. Eksperimentalni rezultati pokazuju kako S_Searching može naći gotovo jednak broj preporučitelja koji bi se pronašli potpunom pretragom, što je mnogo više od onoga što se postiže primjenom klasičnog algoritma pretrage na mreži bez skale, uz znatno smanjenje računske kompleksnosti i zahtjeva.Ključne riječi: algoritam pretraživanja, sigurna mreža, sustav preporučitelja, mreže bez skale

show abstract

Scale-Free Networks

Cited by 1,886 publications

References 0 publications

The Power of Quasi-Shortest Paths: $\rho$ -Geodesic Betweenness Centrality

The Power of Quasi-Shortest Paths: $\rho$ -Geodesic Betweenness Centrality

Mechanists Must be Holists Too! Perspectives from Circadian Biology

Recommender Searching Mechanism for Trust-Aware Recommender Systems in Internet of Things

Contact Info

Product

Resources

About