Metrics and Models for Social Networks

Bersano-Méndez, Nicolás Ignacio; Schaeffer, Satu Elisa; Bustos-Jiménez, Javier

doi:10.1007/978-1-4471-4048-1_5

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…Notice that the semantic content is not considered but only the network structure. Several network metrics can be obtained (like cluster values and other network structure values [21]) revealing network structure differences due to learning. This is a consistent result in other courses we have analyzed in the same knowledge domain or other knowledge domains.…”

Section: Instrumentsmentioning

confidence: 99%

Alternative E-learning Assessment by Mutual Constrain of Responsive and Constructive Techniques of Knowledge Acquisition Evaluation

Elizabeth¹,

Octavio²,

Moreno³

2016

IJI

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Innovation on e-assessment is presented. Here, 43 bachelor students´ conceptual definitions to a course schema related concepts are obtained by using a natural semantic network technique. This allowed implementing a constructive assessment technique of students´ that is complemented by a responsive assessment of student´s learning based on semantic priming studies. It is argued that by using both assessment approaches in a dynamic mutually constrained way a new alternative approach to eassessment can be implemented. This system presents several innovative advantages that empower students and teachers to monitor what actually takes place over the trajectory of a student learning process.

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Section: Instrumentsmentioning

confidence: 99%

Alternative E-learning Assessment by Mutual Constrain of Responsive and Constructive Techniques of Knowledge Acquisition Evaluation

Elizabeth¹,

Octavio²,

Moreno³

2016

IJI

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“…An in-depth understanding of the graph structure is key to convert data into information. To do so, complex networks tools have emerged [3] to classify networks [4], detect communities [5], determine important features and measure them [1].…”

Section: Related Workmentioning

confidence: 99%

“…For each α, we simulate 50 networks of size 1000 (i.e., with 1000 nodes). Then, we tested and compared strategies ranked with a multitude of centrality measures [1] in terms of the R-index. However, apart from Miuz, for this article we selected and reported the results for degree, betweenness and harmonic centrality.…”

Section: Simulationsmentioning

confidence: 99%

Miuz: measuring the impact of disconnecting a node

Bachmann¹,

Reyes²,

Silva³

et al. 2015

2015 34th International Conference of the Chilean Computer Science Society (SCCC)

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In this article we present Miuz, a robustness index for complex networks. Miuz measures the impact of disconnecting a node from the network while comparing the sizes of the remaining connected components. Strictly speaking, Miuz for a node is defined as the inverse of the size of the largest connected component divided by the sum of the sizes of the remaining ones. We tested our index in attack strategies where the nodes are disconnected in decreasing order of a specified metric. We considered Miuz and other well-known centrality measures such as betweenness, degree , and harmonic centrality. All of these metrics were compared regarding the behavior of the robust-ness (R-index) during the attacks. In an attempt to simulate the internet backbone, the attacks were performed in complex networks with power-law degree distributions (scale-free networks). Preliminary results show that attacks based on disconnecting a few number of nodes Miuz are more dangerous (decreasing the robustness) than the same attacks based on other centrality measures. We believe that Miuz, as well as other measures based on the size of the largest connected component, provides a good addition to other robustness metrics for complex networks.Comment: in 34th International Conference of the Chilean Computer Science Society (SCCC), Nov 2015, Santiago, Chile. 2015. arXiv admin note: substantial text overlap with arXiv:1601.0246

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“…We consider that an "adversary" should plan a greedy strategy aiming to maximize the damage with minimum number of strikes. Thus, in this article we discuss the performance of attacks based on the edge betweenness centrality metric [2] over the Internet Backbone (the network formed by Internet exchange points, IXP), and its correlation with what users perceive from such networks if they want to receive content from the major content provider (Google), with a metric called Go-Index.…”

Section: Introductionmentioning

confidence: 99%

I Accidentally the Whole Internet

Bachmann,

Morales,

Silva

et al. 2016

Preprint

Self Cite

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Whether as telecommunications or power systems, networks are very important in everyday life. Maintaining these networks properly functional and connected, even under attacks or failures, is of special concern.This topic has been previously studied with a whole network robustness perspective, modeling networks as undirected graphs (such as roads or simply cables). This perspective measures the average behavior of the network after its last node has failed.In this article we propose two alternatives to well-known studies about the robustness of the backbone Internet: to use a supply network model and metrics for its representation (we called it the Go-Index), and to use robustness metrics that can be calculated while disconnections appear.Our research question is: if a smart adversary has a limited number of strikes to attack the Internet, how much will the damage be after each one in terms of network disconnection?Our findings suggest that in order to design robust networks it might be better to have a complete view of the robustness evolution of the network, from both the infrastructure and the users perspective.

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Metrics and Models for Social Networks

Cited by 10 publications

References 39 publications

Alternative E-learning Assessment by Mutual Constrain of Responsive and Constructive Techniques of Knowledge Acquisition Evaluation

Alternative E-learning Assessment by Mutual Constrain of Responsive and Constructive Techniques of Knowledge Acquisition Evaluation

Miuz: measuring the impact of disconnecting a node

I Accidentally the Whole Internet

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