Dynamic networks analysis and visualization through spatiotemporal link segmentation

Ting Li, Qi Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Big network data analysis has become a challenging task not only due to increasing large volume but the appearance of dynamic spatial-temporal relationship. Both network topologies and their link properties are constantly changing as a result of newly established and torn connections. Traditional data mining techniques in large-scale dynamic networks are either incapable or computationally expensive. To that end, we developed a dynamic network analysis and visualization (DNAV) tool. One major component of DNAV is a dynamic graph in which links are divided according to their temporal dimensions. Each segment on network edges represents the dynamic network temporal evolution of graph properties, e.g., locations where the communications occur. Unlike animation approaches, the proposed static view of dynamic networks does not rely deeply on human cognitive ability on remembering changes over different time slots, thus dramatically simplifying the visual analytic process for dynamic networks. To further improve scalability of rendering large networks, data filtering modules such as time selection, hops settings, entities selection, and edge weight thresholds, are adopted in the visualization. Case study demonstrates the effectiveness of DNAV tool in understanding the dynamic network patterns and trends and the potential to analyze anomalies in dynamic communication networks.

Original languageEnglish
Title of host publicationProceedings of 2016 IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-214
Number of pages6
ISBN (Electronic)9781509025930
DOIs
StatePublished - Aug 2 2016
Event2016 IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016 - Chengdu, China
Duration: Jul 5 2016Jul 7 2016

Publication series

NameProceedings of 2016 IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016

Conference

Conference2016 IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016
Country/TerritoryChina
CityChengdu
Period07/5/1607/7/16

Keywords

  • dynamic network
  • link segmentation
  • spatiotemporal visualization

Fingerprint

Dive into the research topics of 'Dynamic networks analysis and visualization through spatiotemporal link segmentation'. Together they form a unique fingerprint.

Cite this