Visual Analysis of Dynamic Networks Using Change Centrality

Federico, Paolo; Aigner, Wolfgang; Miksch, Silvia; Zenk, Lukas

doi:10.1109/asonam.2012.39

Cited by 23 publications

(15 citation statements)

References 20 publications

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“…Intra-cluster distances were calculated using the agglomerative clustering functions "linkage" with distance calculated from shared PCs using the cluster average (also known as UPGMA), and novel clusters identified using the "fcluster" function of Scipy's hierarchical clustering. In parallel, the method to calculate change centrality was calculated as described previously 69 . CCs were calculated in a successive way, in which each addition was compared to Viral RefSeq 85 independently of other additions (0% versus 10%, 0% vs 20%, […], 0% vs 100%).…”

Section: Methodsmentioning

confidence: 99%

Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks

et al. 2019

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Viruses of bacteria and archaea are likely to be critical to all natural, engineered and human ecosystems, and yet their study is hampered by the lack of a universal or scalable taxonomic framework. Here, we introduce vConTACT 2.0, a network-based application to establish prokaryotic virus taxonomy that scales to thousands of uncultivated virus genomes, and integrates confidence scores for all taxonomic predictions. Performance tests using vConTACT 2.0 demonstrate near-identical correspondence to the current official viral taxonomy (>85% genus-rank assignments at 96% accuracy) through an integrated distance-based hierarchical clustering approach. Beyond "known viruses", we used vConTACT 2.0 to automatically assign 1,364 previously unclassified reference viruses to tentative taxa, and scaled it to. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Section: Methodsmentioning

confidence: 99%

Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks

et al. 2019

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“…Pohl et al proposed an aggregated structural metrics by measuring variation of the degree centrality [37]. To address local structural change details, Paolo et.al proposed a novel metric for dynamic networks [21], but their application is limited to a small exemplary case.…”

Section: Dynamic Network Analysis and Visualizationmentioning

confidence: 99%

A visual analytics approach for understanding egocentric intimacy network evolution and impact propagation in MMORPGs

Shen

Yao

et al. 2017

2017 IEEE Pacific Visualization Symposium (PacificVis)

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“…The currently created network layout compared to the previous layout for predicting similarity to the final layout. Changes in the structure defined by the chang centrality metric that enables pairwise comparisons in the evolving network [FPA+12]. They have presented a set of novel metrics for the visual analysis of dynamic networks.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Social Network Visualization Based- Dimensionality Reduction

Al-Ghalibi¹,

Al-Azzawi²

2018

CSRN

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Social networks are in general dynamically due to the involvement of many people on the web such as Facebook, Twitter, and Snapchat, etc. The meaningful visualization and analysis of social network is challenging due to its dynamic nature, the mobility of nodes in the network and extremely large size. In this paper, we consider the higher dimensionality issue of social networks regarding time series social network construction and visualization. To solve this issue, we develop a statically data-mining based approach for dimensionality reduction in social networks. Basically, we find that each sub-social network's model has different dimensions by nodes and links which are sampled originally from an m-dimensional metric space. Experimentally, we find that the m-dimensional features for each sub-network cause fail connections in time-series during the network reconstruction model for visualization. Therefore, we propose a new dimension reduction approach that is based on developing an SVD algorithm by relying on select significant sub features. Then we extract time features from the feature space of the original dataset to visualize the network in a deferent time interval. However, to monitor the network development and also the dimensionality reduction of features help us to speed up the computation time of the shortest path. The social circle Facebook dataset form Stanford is used with its corresponding attributes. The dataset includes node features (profile), circles, and ego networks. The obtained result shows better performances regarding the computation time and network visualization. Moreover, the experimental results show that the proposed system is much faster than the approach based on the whole feature space for closeness centrality computing.

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Visual Analysis of Dynamic Networks Using Change Centrality

Cited by 23 publications

References 20 publications

Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks

Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks

A visual analytics approach for understanding egocentric intimacy network evolution and impact propagation in MMORPGs

Time-Series Social Network Visualization Based- Dimensionality Reduction

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