Visual analysis of graphs with multiple connected components

Landesberger, Tatiana von; Görner, Melanie; Schreck, Tobias

doi:10.1109/vast.2009.5333893

Cited by 45 publications

(42 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, they are not suitable for grouping the time steps according to the similarity of the flow patterns. As the flows form spatio-temporal graphs, we hoped to use graph-based features for clustering [49], in particular, the degree, centrality, and clustering measures of the nodes. None of the graph centralities of nodes reflect movement direction.…”

Section: Discussionmentioning

confidence: 99%

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Landesberger

Brodkorb

Roskosch

et al. 2016

IEEE Trans. Visual. Comput. Graphics

Self Cite

190

115

View full text Add to dashboard Cite

Fig. 1. Hourly flows of residential Twitter users in Greater London area over a week are spatially and temporally simplified to gain an overview of mobility dynamics. A calendar view shows the temporal cluster distribution over a week. Each map displays movements between spatial aggregates in a temporal cluster. Movement direction is represented by a color gradient from dark to light blue.Abstract-Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time intervals) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called MobilityGraphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that MobilityGraphs support the identification of regular daily and weekly movement patterns of resident population.

show abstract

Section: Discussionmentioning

confidence: 99%

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Landesberger

Brodkorb

Roskosch

et al. 2016

IEEE Trans. Visual. Comput. Graphics

Self Cite

190

115

View full text Add to dashboard Cite

show abstract

“…In the first of these, users iteratively choose pipeline parameters and examine the outputs, until the solution is satisfactory. Some techniques are primarily computational, often incorporating data mining methods, and examples include dimension reduction [6], clustering [7] and classification [8]. However, visualization is an inherent part of others approaches, for example, using statistical learning techniques to make a real-time prediction of the results for regions of parameter settings [9].…”

Section: Methods For Pipeline Design and Optimizationmentioning

confidence: 99%

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Landesberger

Fellner

Ruddle

2017

IEEE Trans. Visual. Comput. Graphics

Self Cite

View full text Add to dashboard Cite

Abstract-The rising quantity and complexity of data creates a need to design and optimize data processing pipelines -the set of data processing steps, parameters and algorithms that perform operations on the data. Visualization can support this process but, although there are many examples of systems for visual parameter analysis, there remains a need to systematically assess users' requirements and match those requirements to exemplar visualization methods. This article presents a new characterization of the requirements for pipeline design and optimization. This characterization is based on both a review of the literature and first-hand assessment of eight application case studies. We also match these requirements with exemplar functionality provided by existing visualization tools. Thus, we provide end-users and visualization developers with a way of identifying functionality that addresses data processing problems in an application. We also identify seven future challenges for visualization research that are not met by the capabilities of today's systems.

show abstract

“…There are techniques for multiple graph comparison [16,33,48]. They only analyze global similarities without subgraph matching.…”

Section: Visual Comparison Of Multiple Graphsmentioning

confidence: 99%

Visual analysis of patterns in multiple amino acid mutation graphs

Lenz

Keul

Bremm

et al. 2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

Self Cite

View full text Add to dashboard Cite

Fig. 1. The problem and the proposed solution for the visual analysis of patterns in mutation graphs: Top: A set of input mutation graphs. It is difficult to compare them and to identify common patterns. Middle: Visualization of the number of found patterns grouped by their structure. Bottom: User-selected relevant patterns can be examined in detail both in the input graph and in the 3D structure.Abstract-Proteins are essential parts in all living organisms. They consist of sequences of amino acids. An interaction with reactive agent can stimulate a mutation at a specific position in the sequence. This mutation may set off a chain reaction, which effects other amino acids in the protein. Chain reactions need to be analyzed, as they may invoke unwanted side effects in drug treatment. A mutation chain is represented by a directed acyclic graph, where amino acids are connected by their mutation dependencies. As each amino acid may mutate individually, many mutation graphs exist. To determine important impacts of mutations, experts need to analyze and compare common patterns in these mutations graphs. Experts, however, lack suitable tools for this purpose. We present a new system for the search and the exploration of frequent patterns (i.e., motifs) in mutation graphs. We present a fast pattern search algorithm specifically developed for finding biologically relevant patterns in many mutation graphs (i.e., many labeled acyclic directed graphs). Our visualization system allows an interactive exploration and comparison of the found patterns. It enables locating the found patterns in the mutation graphs and in the 3D protein structures. In this way, potentially interesting patterns can be discovered. These patterns serve as starting point for a further biological analysis. In cooperation with biologists, we use our approach for analyzing a real world data set based on multiple HIV protease sequences.

show abstract

Visual analysis of graphs with multiple connected components

Cited by 45 publications

References 27 publications

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Visual analysis of patterns in multiple amino acid mutation graphs

Contact Info

Product

Resources

About