28 research outputs found
Placing Arrows in Directed Graph Drawings
We consider the problem of placing arrow heads in directed graph drawings
without them overlapping other drawn objects. This gives drawings where edge
directions can be deduced unambiguously. We show hardness of the problem,
present exact and heuristic algorithms, and report on a practical study.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Visual Similarity Perception of Directed Acyclic Graphs: A Study on Influencing Factors
While visual comparison of directed acyclic graphs (DAGs) is commonly
encountered in various disciplines (e.g., finance, biology), knowledge about
humans' perception of graph similarity is currently quite limited. By graph
similarity perception we mean how humans perceive commonalities and differences
in graphs and herewith come to a similarity judgment. As a step toward filling
this gap the study reported in this paper strives to identify factors which
influence the similarity perception of DAGs. In particular, we conducted a
card-sorting study employing a qualitative and quantitative analysis approach
to identify 1) groups of DAGs that are perceived as similar by the participants
and 2) the reasons behind their choice of groups. Our results suggest that
similarity is mainly influenced by the number of levels, the number of nodes on
a level, and the overall shape of the graph.Comment: Graph Drawing 2017 - arXiv Version; Keywords: Graphs, Perception,
Similarity, Comparison, Visualizatio
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
Provenance for the people: an HCI perspective on the W3C PROV standard through an online game
In the information age, tools for examining the validity of data are invaluable. Provenance is one such tool, and the PROV model proposed by the World Wide Web Consortium in 2013 offers a means of expressing provenance in a machine readable format. In this paper, we examine from a user’s standpoint notions of provenance, the accessibility of the PROV model, and the general attitudes towards history and the verifiability of information in modern data society. We do this through the medium of an online-game designed to explore these issues and present the findings of the study along with a discussion of some of its implications
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
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
Unboxing Cluster Heatmaps
Background: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks.
Results: We developed an approach to “unbox” the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results.
Conclusions: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps
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Automated Layout of Origin-destination Flow Maps : U.S. County-to-county Migration 2009-2013
Visualizing large movement datasets with flow maps is difficult because overlapping flows create significant graphical conflicts that make accurate interpretation difficult or impossible. Interactive flow mapping applications allow users to explore large movement datasets by automatically generating flow maps from subsets of the data in response to queries by the user. However, even a small number of flows can overlap and cross each other in a way that impedes accurate interpretation. We introduce an interactive flow map of migration in the United States from 2009 to 2013 that uses a force-directed method to automatically lay out county-to-county migration. This is the first interactive map for web browsers that automatically creates origin-destination flow map layouts according to identified cartographic design principles. Adhering to these principles improves the readability of origin-destination flow maps. Map users explore high-level state-to-state migration patterns as well as detailed county-to-county movements through a custom user interface and interactive map features. We show migration flows between counties of different states by representing other states as nodes with a circular arrangement around the selected state, and connect county flows to those nodes. This constrains the map layout to a smaller area, reducing clutter and the amount of interaction required to view flows