Visualization Techniques for Network Analysis and Link Analysis Algorithms

Ying Zhao, Ralucca Gera, Quinn Halpin, Jesse Zhou

2019

Abstract

Military applications require big distributed, disparate, multi-sourced and real-time data that have extremely high rates, high volumes, and diverse types. Warfighters need deep models including big data analytics, network analysis, link analysis, deep learning, machine learning, and artificial intelligence to transform big data into smart data. Explainable deep models will play a more essential role for future warfighters to understand, interpret, and therefore appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners when facing complex threats. In this paper, we show how visualization is used in two typical deep models with two use cases: network analysis, which addresses how to display and present big data both in the exploratory and discovery process, and link analysis, which addresses how to display and present the smart data generated from these processes. By using various visualization tools such as D3, Tableau, and lexical link analysis, we derive useful information from discovering big networks to discovering big data patterns and anomalies. These visualizations become intepretable and explainable deep models that can be readily used by warfighters and decision makers to achieve the sense making and decision making superiority.

Paper Citation

in Harvard Style

Zhao Y., Gera R., Halpin Q. and Zhou J. (2019). Visualization Techniques for Network Analysis and Link Analysis Algorithms. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 561-568. DOI: 10.5220/0008377805610568

in Bibtex Style

@conference{kdir19,
author={Ying Zhao and Ralucca Gera and Quinn Halpin and Jesse Zhou},
title={Visualization Techniques for Network Analysis and Link Analysis Algorithms},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={561-568},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008377805610568},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Visualization Techniques for Network Analysis and Link Analysis Algorithms
SN - 978-989-758-382-7
AU - Zhao Y.
AU - Gera R.
AU - Halpin Q.
AU - Zhou J.
PY - 2019
SP - 561
EP - 568
DO - 10.5220/0008377805610568
PB - SciTePress