Graphs in Scientific Visualization: A Survey

Wang, Chaoli; Tao, Jun

doi:10.1111/cgf.12800

Cited by 33 publications

(19 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also numerous surveys in the literature studying different topics related to graph processing. Examples include surveys on query languages for graph database systems and RDF engines [10,44,47], graph algorithms [3,45,53,97], graph processing systems [16,61], and visualization [24,95]. These surveys do not study how users use the technologies in practice.…”

Section: Related Workmentioning

confidence: 99%

The ubiquity of large graphs and surprising challenges of graph processing: extended survey

et al. 2019

View full text Add to dashboard Cite

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research.

show abstract

Section: Related Workmentioning

confidence: 99%

The ubiquity of large graphs and surprising challenges of graph processing: extended survey

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Similarly, Gu et al [10], [11] have developed a graph representation for visualizing transition relationships in time-varying data. A recent survey on graphbased representations in visualization was presented by Wang and Tao [34]. We present an algorithm to organize any given set of multiple segmentations into an exploration hierarchy.…”

Section: Related Workmentioning

confidence: 99%

FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels

Jadhav

Nadeem

Kaufman

2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

We present a volume exploration framework, FeatureLego, that uses a novel voxel clustering approach for efficient selection of semantic features. We partition the input volume into a set of compact super-voxels that represent the finest selection granularity. We then perform an exhaustive clustering of these super-voxels using a graph-based clustering method. Unlike the prevalent brute-force parameter sampling approaches, we propose an efficient algorithm to perform this exhaustive clustering. By computing an exhaustive set of clusters, we aim to capture as many boundaries as possible and ensure that the user has sufficient options for efficiently selecting semantically relevant features. Furthermore, we merge all the computed clusters into a single tree of meta-clusters that can be used for hierarchical exploration. We implement an intuitive user-interface to interactively explore volumes using our clustering approach. Finally, we show the effectiveness of our framework on multiple real-world datasets of different modalities.

show abstract

“…Most often, graphs are used [45,64,92], such as Reeb graphs [23,91] and nested tracking graphs [47]. Many popular graph structures are accounted for in [89]. An inconvenience of extracting rich tracking structures such as these without taking careful attention to potential noise is that it makes the interpretation quite difficult.…”

Section: Related Workmentioning

confidence: 99%

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking

Soler

Plainchault

Conche

et al. 2018

2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)

View full text Add to dashboard Cite

This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps. Our approach relies on two main contributions. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. The global framework computes persistence diagrams and finds optimal matchings in parallel for every consecutive timestep. Critical trajectories are constructed by associating successively matched persistence pairs over time. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Extensive experiments on real-life datasets show that our matching approach is up to two orders of magnitude faster than the seminal Munkres algorithm. Moreover, compared to a modern approximation method, our approach provides competitive runtimes while guaranteeing exact results. We demonstrate the utility of our global framework by extracting critical point trajectories from various time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Robustness to noise and temporal resolution downsampling is empirically demonstrated.

show abstract

Graphs in Scientific Visualization: A Survey

Cited by 33 publications

References 127 publications

The ubiquity of large graphs and surprising challenges of graph processing: extended survey

The ubiquity of large graphs and surprising challenges of graph processing: extended survey

FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels

Lifted Wasserstein Matcher for Fast and Robust Topology Tracking

Contact Info

Product

Resources

About