Further Towards Unambiguous Edge Bundling: Investigating Power-Confluent Drawings for Network Visualization

AbstractIn the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

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Mathematics of networks

10.1093/oso/9780198805090.003.0006 ◽

2018 ◽

Author(s):

Mark Newman

Keyword(s):

Random Walks ◽

Adjacency Matrix ◽

Graph Partitioning ◽

Dynamic Networks ◽

Graph Laplacian ◽

Network Visualization ◽

Final Part ◽

Bipartite Networks ◽

Component Structure ◽

Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

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Network Visualization

The Oxford Handbook of Social Networks ◽

10.1093/oxfordhb/9780190251765.013.39 ◽

2020 ◽

pp. 351-367

Author(s):

James Moody ◽

Ryan Light

Keyword(s):

Best Practices ◽

Social Relationships ◽

Visual Display ◽

Network Visualization ◽

Making Sense ◽

Complex Information ◽

Underlying Network ◽

History Of ◽

Effective Network ◽

Data Visualizations

This chapter provides an overview of social network visualization. Network analysis encourages the visual display of complex information, but effective network diagrams, like other data visualizations, result from several best practices. After a brief history of network visualization, the chapter outlines several of those practices. It highlights the role that network visualizations play as heuristics for making sense of networked data and translating complicated social relationships, such as those that are dynamic, into more comprehensible structures. The goal in this chapter is to help identify the methods underlying network visualization with an eye toward helping users produce more effective figures.

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ccNetViz: a WebGL-based JavaScript library for visualization of large networks

Bioinformatics ◽

10.1093/bioinformatics/btaa559 ◽

2020 ◽

Vol 36 (16) ◽

pp. 4527-4529

Author(s):

Ales Saska ◽

David Tichy ◽

Robert Moore ◽

Achilles Rasquinha ◽

Caner Akdas ◽

...

Keyword(s):

Systems Biology ◽

Complex Networks ◽

Open Source ◽

High Speed ◽

A Priori ◽

Supplementary Information ◽

Network Visualization ◽

Supplementary Data ◽

Web Based ◽

Flow Of Information

Abstract Summary Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. Availability and implementation The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. Supplementary information Supplementary data are available at Bioinformatics online.

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Net.Create: Network Visualization to Support Collaborative Historical Knowledge Building

International Journal of Computer-Supported Collaborative Learning ◽

10.1007/s11412-021-09343-9 ◽

2021 ◽

Author(s):

Kalani Craig ◽

Joshua Danish ◽

Megan Humburg ◽

Cindy Hmelo-Silver ◽

Maksymilian Szostalo ◽

...

Keyword(s):

Knowledge Building ◽

Network Visualization ◽

Historical Knowledge

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Visual-VM: A Social Network Visualization Tool for Viral Marketing

2014 IEEE International Conference on Data Mining Workshop ◽

10.1109/icdmw.2014.40 ◽

2014 ◽

Cited By ~ 1

Author(s):

Cheng Long ◽

Raymond Chi-Wing Wong

Keyword(s):

Social Network ◽

Viral Marketing ◽

Network Visualization ◽

Visualization Tool

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Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab126 ◽

2021 ◽

Author(s):

Tetsuro Kawano-Sugaya ◽

Koji Yatsu ◽

Tsuyoshi Sekizuka ◽

Kentaro Itokawa ◽

Masanori Hashino ◽

...

Keyword(s):

Functional Limitations ◽

Time Dependent ◽

Network Visualization ◽

Disease Dynamics ◽

Accession Number ◽

Haplotype Networks ◽

Virus Serotype ◽

Serotype 1 ◽

Cluster Visualization ◽

Infectious Disease Dynamics

Abstract Summary Many of software for network visualization are available, but existing software have not been optimized to infection cluster visualization, especially the current worldwide invasion of COVID-19 since 2019. To reach the spatiotemporal understanding of epidemics, we have developed Haplotype Explorer. In Haplotype Explorer, users can explore the network interactively with metadata like accession number, locations, and collection dates. Time dependent transition of the network can be exported as continuous sections for making a movie. Here, we introduce features and products of Haplotype Explorer, demonstrating time-dependent snapshots and a movie of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes. Abstract The worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally due to functional limitations, that motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables to dissect epidemiological consequences via interactive node filters and provides the perspective on infectious disease dynamics depend on regions and time, such as introduction, outbreak, expansion, and containment. Here, we demonstrate the effectiveness of Haplotype Explorer by showing features and an example of visualization. The demo using SARS-CoV-2 genomes are available at https://github.com/TKSjp/HaplotypeExplorer/blob/master/Example/. There are several examples using SARS-CoV-2 genomes and Dengue virus serotype 1 E-genes sequence.

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Discovering latent node Information by graph attention network

Scientific Reports ◽

10.1038/s41598-021-85826-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Weiwei Gu ◽

Fei Gao ◽

Xiaodan Lou ◽

Jiang Zhang

Keyword(s):

Supervised Learning ◽

Link Prediction ◽

Learning Algorithm ◽

Network Visualization ◽

Graph Structure ◽

High Quality ◽

Node Centrality ◽

Influential Nodes ◽

Node Classification ◽

Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

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