Volume-based large dynamic graph analysis supported by evolution provenance

Bruder, Valentin; Lahmar, Houssem Ben; Hlawatsch, Marcel; Frey, Steffen; Burch, Michael; Weiskopf, Daniel; Herschel, Melanie; Ertl, Thomas

doi:10.1007/s11042-019-07878-6

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…The next two papers [3,14] propose new techniques and approaches to improve the visualization of multidimensional graphs. Bruder et al [3] examine the visualization and interactive analysis of dynamic graphs that contain a large number of time steps of time varying data. Dynamic graph are represented by space-time cubes by stacking the adjacency matrices.…”

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confidence: 99%

Special issue on information visualisation

Francese

Banissi

Risi

2019

Multimed Tools Appl

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We are in the Big Data era, characterized by an increasing amount of information generated everyday by all the phenomena concerning our life. These data are collected by many information sources and systems ranging from IoT sensors, large databases, social networks and interactive platforms, as well as generic media like images, videos and audios. Thus, to be able to comprehend the phenomena underlying this data there is the need of designing effective visualization tools. The papers selected for this special issue outline and evaluate some recent works on multimedia tools for information visualization and interaction with various types of data, including textual documents, graphs, spatio-temporal data, multimedia contents, and musical data. The international conference on information Visualisation (iV), one of the longest running on the subject has open forum philosophy of interdisciplinary drive to define its subject theme and discussion and focus group. This special issue of Multimedia Tools and Applications for Information Visualisation is based on the handful of submissions to the 22 nd International Conference on Information Visualisation (iV18), which was held in Italy in July 2018. The selected papers give examples of techniques, domains and number of case studies to address some issues. The papers presented in this Special Issue investigate different aspects of the theme highlighted above. The first set of papers [1, 2, 12, 16] investigate and evaluate the visualization of spatial, temporal and spatio-temporal data. Almeida et al. [2] consider how and why consideration of Level of Detail (LoD) plays a crucial role in analytics. They propose a Visual Analytics approach-VAST, allows users to simultaneous inspection of phenomenon using different LoDs. VAST demonstrates using LoDs will reveal different prospective and hence portraying different perception of the phenomenon.

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confidence: 99%

Special issue on information visualisation

Francese

Banissi

Risi

2019

Multimed Tools Appl

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“…This approach provides a useful overview of existing methods but was mainly developed as a tool for teaching undergraduate students about temporal graph visualisation. Bruder et al [60] build on this concept to create a volumetric representation of a graph by stacking each of the adjacency matrices of its time steps, and also facilitate three important analytics methods: data views, aggregation and filtering, and comparison.…”

Section: Surveysmentioning

confidence: 99%

Visualisation of Long in Time Dynamic Networks on Large Touch Displays

Lee¹

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Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging ﬁelds from travel networks to epidemiology.However, it is very difﬁcult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We ﬁrst introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To conﬁrm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we ﬁnd that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also ﬁnd that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the ﬁrst, but this time using standard-sized displays with indirect mouse input. The second study reafﬁrms the results of the ﬁrst, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types.

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Volume-based large dynamic graph analysis supported by evolution provenance

Cited by 2 publications

References 32 publications

Special issue on information visualisation

Special issue on information visualisation

Visualisation of Long in Time Dynamic Networks on Large Touch Displays

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