More bang for your research buck

Blaha, Leslie M.; Arendt, Dustin; Mohd-Zaid, Fairul

doi:10.1145/2669557.2669566

Cited by 3 publications

(2 citation statements)

References 33 publications

(30 reference statements)

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“…Such mixture network models might be able to truly characterize any real-world model and is the focus of future research. It is now also possible to adopt the result obtained by Blaha et al 42 to visually display the real network using our approach that might give more insight to the real network. This can be done by studying a collection of simulated characterized networks and using the methodology used by Blaha et al 42 to study the most efficient way to visualize the network that will provide as much insight as possible to the network analyst.…”

Section: Discussionmentioning

confidence: 99%

“…It is now also possible to adopt the result obtained by Blaha et al 42 to visually display the real network using our approach that might give more insight to the real network. This can be done by studying a collection of simulated characterized networks and using the methodology used by Blaha et al 42 to study the most efficient way to visualize the network that will provide as much insight as possible to the network analyst. Although the data sets used in this research were not focused specifically on military and defense applications, our approach and findings can be applied to such applications.…”

Section: Discussionmentioning

confidence: 99%

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Network characterization and simulation via mixed properties of the Barabási–Albert and Erdös–Rényi degree distribution

Mohd-Zaid

Kabban

Deckro

et al. 2022

Journal of Defense Modeling & Simulation

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Social network analysis (SNA) is a tool for the operations researcher to understand, monitor, and exploit social and military structures which are key in the intelligence community. However, in order to study and influence a network of interest, the network must first be characterized; preferably to a known network model that captures a mixture of graphical properties exhibited by the social network of interest. In this work, we present a novel statistical method for both characterizing networks via a Binomial-Pareto maximum-likelihood approach and simulating the characterized network using a graph of mixed Barabási–Albert (BA, scale-free) and Erdös–Rényi (ER, randomness) properties. Characterization is performed through a combination of hypothesis tests and method of moments parameter estimation on Pareto and Doubly Truncated Binomial distributions. Application on real-world networks suggests that such networks may be characterized with a mixture of scale-free and random properties as modeled through BA and ER graphs. We demonstrate that our simulation methods are able to capture the degree distribution and density of the networks examined. These results demonstrate that this work establishes a statistical framework upon which network characterization and simulation may be accomplished, thus enabling the adaptation of such methods when generating, manipulating, and observing networks of interest.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Network characterization and simulation via mixed properties of the Barabási–Albert and Erdös–Rényi degree distribution

Mohd-Zaid

Kabban

Deckro

et al. 2022

Journal of Defense Modeling & Simulation

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SeekAView: An intelligent dimensionality reduction strategy for navigating high-dimensional data spaces

Krause¹,

Dasgupta

Fekete

et al. 2016

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)

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Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time to suit their exploration strategies. The results often suffer from a lack of interpretability, especially for domain experts not trained in statistics and machine learning. Second, exploratory visualization techniques like scatter plots or parallel coordinates suffer from a lack of visual scalability: it is difficult to present a coherent overview of interesting combinations of dimensions. Third, the existing techniques do not provide a flexible workflow that allows for multiple perspectives into the analysis process by automatically detecting and suggesting potentially interesting subspaces. In SeekAView we address these issues using suggestion based visual exploration of interesting patterns for building and refining multidimensional subspaces. Compared to the state-of-the-art in subspace search and visualization methods, we achieve higher transparency in showing not only the results of the algorithms, but also interesting dimensions calibrated against different metrics. We integrate a visually scalable design space with an iterative workflow guiding the analysts by choosing the starting points and letting them slice and dice through the data to find interesting subspaces and detect correlations, clusters, and outliers. We present two usage scenarios for demonstrating how SeekAView can be applied in real-world data analysis scenarios.

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