Structural bottlenecks for communication in networks

Sreenivasan, Sameet; Cohen, Reuven; López, Eduardo; Toroczkai, Zoltán; Stanley, H. Eugene

doi:10.1103/physreve.75.036105

Cited by 138 publications

(146 citation statements)

References 13 publications

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“…[6,7]. The transport capacity also exceeds the analytical estimate for its maximum value given in [7]. Furthermore, we argue that our algorithm achieves near-optimal routing for uncorrelated scale-free networks.…”

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confidence: 84%

“…Our algorithm leads to higher transport capacity than those presented in Refs. [6,7]. The transport capacity also exceeds the analytical estimate for its maximum value given in [7].…”

mentioning

confidence: 93%

“…An open question is how much larger the actual transport capacity can be than the results presented in Refs. [6,7]. In this Letter, we show that significant improvement in the transport capacity of a network can be achieved by systematically adjusting the traffic routing to minimize the maximum betweenness on the network.…”

mentioning

confidence: 99%

“…As a result, in situations of high network traffic, these hubs will experience congestion (long message queues) and eventually jamming, causing the network to break apart in a multitude of disconnected subnetworks. In light of this behavior, the optimality of the shortest path routing as currently implemented has been recently questioned [6,7,8,9,10,11,12,13,14,15]. It has been shown, for example, that dynamic routing protocols which allow for a certain degree of stochasticity or take into account the congestion status of the nearest neighbors significantly improve the transport capacity of a network [9,10,11,12,13,14,15].…”

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confidence: 99%

“…when avoiding a hub does not lead to congestion on another node). Recent studies [6,7] have shown that this is possible and propose new routing algorithms which lead to improved transport capacity (quantified by the packet insertion rate at which jamming occurs). An open question is how much larger the actual transport capacity can be than the results presented in Refs.…”

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confidence: 99%

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Optimal transport on complex networks

Danila

Marsh³

et al. 2006

Phys. Rev. E

218

187

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We present a novel heuristic algorithm for routing optimization on complex networks. Previously proposed routing optimization algorithms aim at avoiding or reducing link overload. Our algorithm balances traffic on a network by minimizing the maximum node betweenness with as little path lengthening as possible, thus being useful in cases when networks are jamming due to queuing overload. By using the resulting routing table, a network can sustain significantly higher traffic without jamming than in the case of traditional shortest path routing.

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mentioning

confidence: 84%

“…Our algorithm leads to higher transport capacity than those presented in Refs. [6,7]. The transport capacity also exceeds the analytical estimate for its maximum value given in [7].…”

mentioning

confidence: 93%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Optimal transport on complex networks

Danila

Marsh³

et al. 2006

Phys. Rev. E

218

187

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Network modules help the identification of key transport routes, signaling pathways in cellular and other networks

Palotai¹,

Csermely²

2009

Ann. Phys.

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Complex systems are successfully reduced to interacting elements via the network concept. Transport plays a key role in the survival of networks -for example the specialized signaling cascades of cellular networks filter noise and efficiently adapt the network structure to new stimuli. However, our general understanding of transport mechanisms and signaling pathways in complex systems is yet limited. Here we summarize the key network structures involved in transport, list the solutions available to overloaded systems for relaxing their load and outline a possible method for the computational determination of signaling pathways. We highlight that in addition to hubs, bridges and the network skeleton, the overlapping modular structure is also essential in network transport. Path-lenghts in the module-space of the yeast protein-protein interaction network indicated that module-based paths may cross fewer modular boundaries than shortest paths. Moreover, by locating network elements in the space of overlapping network modules and evaluating their distance in this 'module space', it may be possible to approximate signaling pathways computationally, which, in turn could serve the identification of signaling pathways of complex systems. Our model may be applicable in a wide range of fields including traffic control or drug design.

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Network modules help the identification of key transport routes, signaling pathways in cellular and other networks

Palotai

Csermely²

2009

Annalen der Physik

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Complex systems are successfully reduced to interacting elements via the network concept. Transport plays a key role in the survival of networks – for example the specialized signaling cascades of cellular networks filter noise and efficiently adapt the network structure to new stimuli. However, our general understanding of transport mechanisms and signaling pathways in complex systems is yet limited. Here we summarize the key network structures involved in transport, list the solutions available to overloaded systems for relaxing their load and outline a possible method for the computational determination of signaling pathways. We highlight that in addition to hubs, bridges and the network skeleton, the overlapping modular structure is also essential in network transport. Path‐lenghts in the module‐space of the yeast protein‐protein interaction network indicated that module‐based paths may cross fewer modular boundaries than shortest paths. Moreover, by locating network elements in the space of overlapping network modules and evaluating their distance in this ‘module space’, it may be possible to approximate signaling pathways computationally, which, in turn could serve the identification of signaling pathways of complex systems. Our model may be applicable in a wide range of fields including traffic control or drug design.

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Structural bottlenecks for communication in networks

Cited by 138 publications

References 13 publications

Optimal transport on complex networks

Optimal transport on complex networks

Network modules help the identification of key transport routes, signaling pathways in cellular and other networks

Network modules help the identification of key transport routes, signaling pathways in cellular and other networks

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