Measures for Network Structural Dependency Analysis

Woldeyohannes, Yordanos Tibebu; Jiang, Yuming

doi:10.1109/lcomm.2018.2864109

Cited by 9 publications

(12 citation statements)

References 10 publications

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“…The NDI score has recently been described in unweighted networks (Woldeyohannes & Jiang, 2018). Here, we extend their formalism for use in weighted networks.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we present an alternative for defining regions of the brain that are integral for network efficiency, and which can be consistently identified across the life-span, based on the NDI, network dependency index (Woldeyohannes & Jiang, 2018). First introduced in network communication science for critical node detection, NDI quantifies a node’s importance as defined by the impact on the network’s performance given the node’s failure (or removal) from the system; simply put, NDI measures the dependency of the network on any given node.…”

Section: Introductionmentioning

confidence: 99%

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Network structural dependency in the human connectome across the life-span

Schirmer

Chung

Grant

et al. 2019

Network Neuroscience

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Principles of network topology have been widely studied in the human connectome. Of particular interest is the modularity of the human brain, where the connectome is divided into subnetworks from which changes with development, aging or disease can be investigated. We present a weighted network measure, the Network Dependency Index (NDI), to identify an individual region’s importance to the global functioning of the network. Importantly, we utilize NDI to differentiate four subnetworks (Tiers) in the human connectome following Gaussian mixture model fitting. We analyze the topological aspects of each subnetwork with respect to age and compare it to rich club-based subnetworks (rich club, feeder, and seeder). Our results first demonstrate the efficacy of NDI to identify more consistent, central nodes of the connectome across age groups, when compared with the rich club framework. Stratifying the connectome by NDI led to consistent subnetworks across the life-span, revealing distinct patterns associated with age where, for example, the key relay nuclei and cortical regions are contained in a subnetwork with highest NDI. The divisions of the human connectome derived from our data-driven NDI framework have the potential to reveal topological alterations described by network measures through the life-span.

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“…The NDI score has recently been described in unweighted networks (Woldeyohannes & Jiang, 2018). Here, we extend their formalism for use in weighted networks.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network structural dependency in the human connectome across the life-span

Schirmer

Chung

Grant

et al. 2019

Network Neuroscience

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“…The node dependency index DI(i|n) measures the average level of dependency node i has on node n in connecting with the other nodes of the network [13]. DI(i|n) is calculated from the path dependency index DI(i → j|n), which measures the dependency the path between nodes i and j has on node n. DI(i → j|n) is defined as:…”

Section: Structural Dependency Measuresmentioning

confidence: 99%

“…To tackle the first challenge, an algorithm that measures the dependency among network nodes and identifies nodes that have a high-level of structural correlation is proposed. The dependency among nodes of a network is quantified by using a centrality measure called node dependency index [13]. Here, there is an intuition, which is, a backup NF should not be placed at a node that may also become unavailable when the primary NF's node fails.…”

Section: Introductionmentioning

confidence: 99%

Towards Carrier-Grade Service Provisioning in NFV

Woldeyohannes

Tola

Jiang

2019

2019 15th International Conference on the Design of Reliable Communication Networks (DRCN)

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Network Function Virtualization (NFV) is an emerging technology that reduces cost and brings flexibility in the provisioning of services. NFV-based networks are expected to be able to provide carrier-grade services, which require high availability. One of the challenges for achieving high availability is that the commodity servers used in NFV are more error prone than the purpose-built hardware. The "de-facto" technique for fault tolerance is redundancy. However, unless planned carefully, structural dependencies among network nodes could result in correlated node unavailabilities that undermine the effect of redundancy. In this paper, we address the challenge of developing a redundancy resource allocation scheme that takes into account correlated unavailabilities caused by network structural dependencies. The proposed scheme consist of two parts. In the first part, we propose an algorithm to identify nodes that can be highly affected by a node failure because of their network structural dependency with this node. The algorithm analyzes such dependencies using a recently proposed centrality measure called dependency index. In the second part, a redundancy resource allocation scheme that places backup network functions on nodes considering their dependency nature and assigns the instances to flows optimally is proposed. The results show that not considering the network structural dependency in backup placement may significantly affect the service availability to flows. The results also give insights into the trade-off between cost and performance.

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“…which is defined as network efficiency [15]. Network efficiency quantifies how efficient the exchange of information across the whole network under the shortest-path routing [21]. The network efficiency of a network monotonically decreases with the successive link removals.…”

Section: A Robustness Metricsmentioning

confidence: 99%

Topological Approach to Measure Network Recoverability

Sun

Mieghem

2019

2019 11th International Workshop on Resilient Networks Design and Modeling (RNDM)

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Network recoverability refers to the ability of a network to return to a desired performance level after suffering malicious attacks or random failures. This paper proposes a general topological approach and recoverability indicators to measure the network recoverability in two scenarios: 1) recovery of damaged connections and 2) any disconnected pair of nodes can be connected to each other. Our approach presents the effect of the random attack and recovery processes on the network performance by the robustness envelopes of realizations and the histograms of two recoverability indicators. By applying the effective graph resistance and the network efficiency as robustness metrics, we employ the proposed approach to assess 10 realworld communication networks. Numerical results verify that the network recoverability is coupled to the network topology, the robustness metric and the recovery strategy. We also show that a greedy recovery strategy could provide a near-optimal recovery performance for the investigated robustness metrics.

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Measures for Network Structural Dependency Analysis

Cited by 9 publications

References 10 publications

Network structural dependency in the human connectome across the life-span

Network structural dependency in the human connectome across the life-span

Towards Carrier-Grade Service Provisioning in NFV

Topological Approach to Measure Network Recoverability

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