Design Decision Patterns for Visualizing Multivariate Graphs

pathline poemage nbgm
NSF Grant 1350896
PI: Miriah Meyer
July 2014 - June 2019 (estimated)

Multivariate graphs are an important data type in many high-impact application areas. Yet we know little about the underlying principles of how to effectively visualize them. Despite the plethora of techniques and methods for visualizing graphs, much of this work breaks down when attempting to visualize more than one or two additional attributes — there are simply not enough visual channels to succinctly show all aspects of a rich, complex multivariate graph. Compounding this challenge is the large design space of possible encodings, making it difficult for visualization practitioners to design effective visualizations of multivariate graphs using a top-down, problem-agnostic approach. This project will establish the first set of validated, foundational principles for visualizing multivariate graphs using a structured, methodological research approach. Several target application areas will drive the investigations using a design study approach. These areas were chosen to represent a wide spectrum of possible applications in which multivariate graphs play a central role, thus fostering generalizable results.

resulting techniques


A key task in multivariate graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce a technique called the connectivity matrix that provide an overview of the connectivity and reveal details on demand.

paths through
  spatially constrained nodes

Graphs that consist of nodes that are spatial constrained -- such nodes with a geospatial location -- are difficult to visualize due to the inability to move nodes to reduce visual clutter. Tackling this challenge, we developed a novel technique that supports visualization of paths through spatial constrained graphs. The technique reduces visual clutter while also maintaining visibility of all nodes through an algorithm that reroutes edges around non-path nodes, while also ensuring that individual, intersecting paths are visible. This technique supports visualization and analysis of the intersecting and diverging nature of paths.

resulting methods


In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly address the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into filling this gap. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design research, while aiming to ensure reliability of the process and design.


The nested blocks and guidelines model for describing visualization design decisions extends previous models to provide explicit mechanisms that capture and discuss design decision rationale. Blocks are the outcomes of design decisions throughout the design process, and guidelines discuss relationships between these blocks. Using the NBGM we are able to more concretely identify possible weaknesses in exisitng and new guidelines, clarify assumptions that require further evaluation, and provide feedback on the rigor and validity of visualization research results.


Miriah Meyer

Associate Professor, School of Computing, University of Utah


Ethan Kerzner

PhD student



project alumni

Nina McCurdy, PhD student

Josh Dawson, MS student


Graffinity: Visualizing Connectivity in Large Graphs. Ethan Kerzner, Alex Lex, Crystal Sigulinsky, Tim Urness, Bryan Jones, Robert Marc, Miriah Meyer. Computer Graphics Forum (Proceedings of EuroVis 2017), to appear.

The rod-cone crossover connectome of mammalian bipolar cells. J. Scott Lauritzen, Crystal Sigulinsky, James Anderson, Noah Nelson, Daniel Emrich, Christopher Rapp, Nicholas McCarthy, Michael Kalloniatis, Ethan Kerzner, Miriah Meyer, Bryan Jones, Robert Marc. Journal of Comparative Neurology, 2016.

Action Design Research and Visualization Design. Nina McCurdy, Jason Dykes, Miriah Meyer. Proceedings of the Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization (BELIV), IEEE VIS 2016.

Poemage: Visualizing the Sonic Topology of a Poem. Nina McCurdy, Julie Lein, Katharine Coles, Miriah Meyer. IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2015), 22(1):439-448, 2016.

The Nested Blocks and Guidelines Model. Miriah Meyer, Michael Sedlmair, P. Samuel Quinan, Tamara Munzner. Journal of Information Visualization, 14(3):234-249, 2015.



Each fall we teach a class on the fundamental concepts of visualization in the School of Computing at the University of Utah. This course is a mix of undergraduate and graduate students. The syllabus, lecture materials, and readings can be found on the course website.


We have conducted a number of events to teach high school girls about visualization and computer science. Through an annual collaboration with local high schools, we give short talks to girls about the power of visualization, and answer questions about being a computer scientist. We are also involved with the annual NCWIT Aspirations Awards that recognizes young women who are active and interested in computing and technology, and encourages them to pursue their passions.


Graffinity: Graffinity is a prototype implementation of two visualization techniques for visualizing connectivity realtionships in large graphs. These techniques are the connectivity matrix and intermediate node table. Graffinity also includes a query interface and supplemental views in the form of path lists and node-link diagrams. Graffinity was developed at the University of Utah as part of an ongoing collaboration between data visualization experts and neuroscientists.

Poemage: Poemage is a visualization system for exploring the the complex structures formed from a spatially-constrained graph representing the interaction of words connected through some sonic or linguistic resemblance. The graph is generated using text-to-speach algorithms combined with our novel NLP approach for defining sonic similarities. The visualization tool allows the user to explore paths through the graph using several, linked views, and includes a novel technique for visualizing multiple paths directly in the text document itself. Poemage was developed at the University of Utah as part of an ongoing, highly exploratory collaboration between data visualization experts and literary scholars.

last update June 25, 2017