A User Study on Hybrid Graph Visualizations

Giacomo, Emilio Di; Didimo, Walter; Montecchiani, Fabrizio; Tappini, Alessandra

doi:10.1007/978-3-030-92931-2_2

Cited by 5 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A preliminary version of this paper appeared in the proceedings of the 29th International Symposium on Graph Drawing and Network Visualization [1]; the article received the best paper award and the authors have been invited to submit an extended version to IEEE TVCG.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Giacomo

Didimo

Liotta

et al. 2024

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Hybrid visualizations combine different metaphors into a single network layout, in order to help humans in finding the "right way" of displaying the different portions of the network, especially when it is globally sparse and locally dense. We investigate hybrid visualizations in two complementary directions: (i) On the one hand, we evaluate the effectiveness of different hybrid visualization models through a comparative user study; (ii) On the other hand, we estimate the usefulness of an interactive visualization that integrates all the considered hybrid models together. The results of our study provide some hints about the usefulness of the different hybrid visualizations for specific tasks of analysis and indicates that integrating different hybrid models into a single visualization may offer a valuable tool of analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Giacomo

Didimo

Liotta

et al. 2024

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…In general, different visual representations with varying levels of detail can be combined by nesting, juxtaposing, integrating, superimposing, or overloading detail representations into a parent visualization [JE12]. In terms of graph visualizations, hybrid techniques [DGDMT21], such as NodeTrix [HFM07] or Responsive Matrix Cells [HBS * 21], embed alternative visualization types into an underlying global NLD or matrix representation. Similarly, interactive lenses can alter a representation locally on demand [TGK * 17].…”

Section: Related Workmentioning

confidence: 99%

Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization

Bludau

Dörk

Tominski

2023

Computer Graphics Forum

View full text Add to dashboard Cite

Existing work on visualizing multivariate graphs is primarily concerned with representing the attributes of nodes. Even though edges are the constitutive elements of networks, there have been only few attempts to visualize attributes of edges. In this work, we focus on the critical importance of edge attributes for interpreting network visualizations and building trust in the underlying data. We propose ‘unfolding of edges’ as an interactive approach to integrate multivariate edge attributes dynamically into existing node‐link diagrams. Unfolding edges is an in‐situ approach that gradually transforms basic links into detailed representations of the associated edge attributes. This approach extends focus+context, semantic zoom, and animated transitions for network visualizations to accommodate edge details on‐demand without cluttering the overall graph layout. We explore the design space for the unfolding of edges, which covers aspects of making space for the unfolding, of actually representing the edge context, and of navigating between edges. To demonstrate the utility of our approach, we present two case studies in the context of historical network analysis and computational social science. For these, web‐based prototypes were implemented based on which we conducted interviews with domain experts. The experts' feedback suggests that the proposed unfolding of edges is a useful tool for exploring rich edge information of multivariate graphs.

show abstract

“…Whilst node-link representations are intuitive to convey network topology, they have very limited scalability (Ghoniem et al, 2004 ; Nobre et al, 2020 ; Di Giacomo et al, 2021 ). Rendering large networks can be slow and the result can be cumbersome.…”

Section: Interactive Visualization Of Large Networkmentioning

confidence: 99%

“…Furthermore, besides the cognitive burden on users attempting to grasp both local and global information, a heavy rendering load impairs smooth navigation on node-link views (Ghoniem et al, 2005 ). Limitations of node-link representations encouraged many research efforts on devising alternative visualization techniques, with relative success (Nobre et al, 2020 ; Di Giacomo et al, 2021 ). Yet, node-link views remain a classic approach for interactive visualization of networks, even if to be used in combination with other techniques (Di Giacomo et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…Limitations of node-link representations encouraged many research efforts on devising alternative visualization techniques, with relative success (Nobre et al, 2020 ; Di Giacomo et al, 2021 ). Yet, node-link views remain a classic approach for interactive visualization of networks, even if to be used in combination with other techniques (Di Giacomo et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

Valejo

Fabbri

Lopes

et al. 2022

Front. Res. Metr. Anal.

View full text Add to dashboard Cite

Bipartite networks are pervasive in modeling real-world phenomena and play a fundamental role in graph theory. Interactive exploratory visualization of such networks is an important problem, and particularly challenging when handling large networks. In this paper we present results from an investigation on using a general multilevel method for this purpose. Multilevel methods on networks have been introduced as a general approach to increase scalability of community detection and other complex optimization algorithms. They employ graph coarsening algorithms to create a hierarchy of increasingly coarser (reduced) approximations of an original network. Multilevel coarsening has been applied, e.g., to the problem of drawing simple (“unipartite”) networks. We build on previous work that extended multilevel coarsening to bipartite graphs to propose a visualization interface that uses multilevel coarsening to compute a multi-resolution hierarchical representation of an input bipartite network. From this hierarchy, interactive node-link drawings are displayed following a genuine route of the “overview first, zoom and filter, details on demand” visual information seeking mantra. Analysts may depart from the coarsest representation and select nodes or sub-graphs to be expanded and shown at greater detail. Besides intuitive navigation of large-scale networks, this solution affords great flexibility, as users are free to select different coarsening strategies in different scenarios. We illustrate its potential with case studies involving real networks on distinct domains. The experimental analysis shows our strategy is effective to reveal topological structures, such as communities and holes, that may remain hidden in a conventional node-link layout. It is also useful to highlight connectivity patterns across the bipartite layers, as illustrated in an example that emphasizes the correlation between diseases and genes in genetic disorders, and in a study of a scientific collaboration network of authors and papers.

show abstract

A User Study on Hybrid Graph Visualizations

Cited by 5 publications

References 50 publications

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Unfolding Edges: Adding Context to Edges in Multivariate Graph Visualization

Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks

Contact Info

Product

Resources

About