Valuable Change Detection in Keyword Map Animation

A four‐stage social network trend mining framework, the Identification, Grouping, Clustering and Visualization framework, is described. The framework extracts trends from social network data and then applies a sequence of techniques (‘tools’) to this data to facilitate interpretation of the identified trends. Of particular note is the visualization of trend migrations (changes) that feature within time‐stamped network data. The framework is illustrated using a sequence of four social networks extracted from the Cattle Tracing System in operation in Great Britain, although it could equally well be applied to other forms of temporal data. The presented analysis of the Identification, Grouping, Clustering and Visualization framework indicates advantages, with respect to network trend mining, that can be gained, especially when the framework is applied to real data.

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“…Further details concerning the background and development of Visuset can be found in Nishikido et al . ().…”

Section: Trend Visualization and Animation Using Visusetmentioning

confidence: 97%

“…Using Visuset it is also possible to disable the spring model so that the user can manually position nodes (and, if applicable, also change the size of individual islands at the same time). Further details concerning the background and development of Visuset can be found in Nishikido et al (2009).…”

Section: Visualization Of Trend Migrationmentioning

confidence: 99%

Trend mining in social networks: from trend identification to visualization

et al. 2013

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Section: Visualisation Of Trend Migrationmentioning

confidence: 99%

Trend Mining and Visualisation in Social Networks

Nohuddin

Sunayama

Christley

et al. 2011

Research and Development in Intelligent Systems XXVIII

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A framework, the IGCV (Identification, Grouping, Clustering and Visualisation) framework, is described to support the temporal analysis of social network data. More specifically the identification and visualisation of "traffic movement" of patterns in such networks, and how such patterns change over time. A full description of the operation of IGCV is presented, together with an evaluation of its operation using a cattle movement network.

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