Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.

Visualizing social networks, especially an overview emphasizing their structure, i.e., communities and their interconnections, is known to be a challenging problem. In this paper, we present a set of design rationales to build such overview visualizations of social networks and our solution called Jasper. We evaluate its performances against two of the most wide-spread visualization techniques (matrix and node-link diagram) in a human–computer controlled experiment based on community-related tasks. While none of the techniques emerge as the overwhelming winner, Jasper appears to be one of the best methods for each task; a fact sustained by the marks given by the users. Overall, Jasper can be seen as an all-encompassing solution for quickly producing legible and compact overviews of large social networks on a single modern computer.

Abstract Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

We present strategic port graph rewriting as a basis for the implementation of visual modelling tools. The goal is to facilitate the specification and programming tasks associated with the modelling of complex systems. A system is represented by an initial graph and a collection of graph rewrite rules, together with a user-defined strategy to control the application of rules. The traditional operators found in strategy languages for term rewriting have been adapted to deal with the more general setting of graph rewriting, and some new constructs have been included in the strategy language to deal with graph traversal and management of rewriting positions in the graph. We give a formal semantics for the language, and describe its implementation: the graph transformation and visualisation tool Porgy.

Biological network figures are ubiquitous in the biology and medical literature. On the one hand, a good network figure can quickly provide information about the nature and degree of interactions between items and enable inferences about the reason for those interactions. On the other hand, good network figures are difficult to create. In this paper, we outline 10 simple rules for creating biological network figures for communication, from choosing layouts, to applying color or other channels to show attributes, to the use of layering and separation. These rules are accompanied by illustrative examples. We also provide a concise set of references and additional resources for each rule.

The most common depictions of graphs are node-link diagrams (NLDs) and matrix-based diagrams (MBDs). Making valid comparisons between these two visualisation techniques is difficult because they are each subject to a variety of representation parameters with respect to graph layout (NLD) and node ordering (MBD), meaning that any given choice of layout and order (even if they fulfil some aesthetic criteria) may influence experimental results. To overcome this problem, we propose a MBD-based technique which hybridises the entity visual encoding of a MBD with the edge visual encoding of a NLD. Using a typical MBD, we propose three edge visual encoding evolutions to ultimately render edges like in a NLD while preserving nodes depiction and order. Such encoding evolutions allow us to perform an experimental evaluation of user performances for a path finding task without the above limitations. We show that for a path finding task, our edge visual encoding evolutions tend to improve the user experience when analysing and interacting with a MBD.

Abstract Graph rewriting systems (GRSs) operate on graphs by substituting local patterns according to a set of rewriting rules. The apparent simplicity of GRSs hides an incredible complexity and turns the study of these systems into an involved task requiring high‐level expertise. We designed PORGY, an interactive visual environment to fully support GRSs related tasks, exploiting a long historical tradition of GRSs with node‐link representations of graphs. PORGY enables rule‐based modeling and simulation steering through graphical representations and direct manipulation of all GRSs components. This paper contributes a design study and task taxonomy relevant to the interactive visualization of GRSs.

In this paper, we present the results of a human-computer interaction experiment that compared the performance of the animation of dynamic graphs to the presentation of small multiples and the effect that mental map preservation had on the two conditions. Questions used in the experiment were selected to test both local and global properties of graph evolution over time. The data sets used in this experiment were derived from standard benchmark data sets of the information visualization community. We found that small multiples gave significantly faster performance than animation overall and for each of our five graph comprehension tasks. In addition, small multiples had significantly more errors than animation for the tasks of determining sets of nodes or edges added to the graph during the same timeslice, although a positive time-error correlation coefficient suggests that, in this case, faster responses did not lead to more errors. This result suggests that, for these two tasks, animation is preferable if accuracy is more important than speed. Preserving the mental map under either the animation or the small multiples condition had little influence in terms of error rate and response time.

Abstract Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced.

Metaheuristics are now so common that for some classical hard combinatorial problems, there exist more than ten variants. Thus, the issue of comparing optimization methods is crucial. In this paper, we focus on one aspect of this question: the impact of the choice of the test instances on the metaheuristic performances and the possible link with the fitness landscape structure. We base our experimental framework on the arc crossing minimization problem for layered digraphs. We compare a hybridized genetic algorithm and a multistart descent which are among the best approaches to this problem. We worked on two instance families with various sizes and structural complexities: small graphs which are easy to draw on a standard size support, and large graphs specifically built for our experiments. We show that, for the smallest instances, there is no significant difference between methods whereas for graphs similar to those classically used nowadays in applications the genetic algorithm is better, and for the largest graphs (with a scaling factor up to 10300), the multistart descent is the best method. These results suggest that for "structured" fitness landscapes associated with real-life instances the GA exploits its implicit learning. On the other hand for very large landscapes with probably numerous local optima, only one exploration on a larger scale can be provided by local searches from a random starting point, cheap in computing effort.