Bruno Pinaud

Animation, Small Multiples, and the Effect of Mental Map Preservation in Dynamic Graphs

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.

Difference map readability for dynamic graphs

Difference maps are one way to show changes between timeslices in a dynamic graph. They highlight, using colour, the nodes and edges that were added, removed, or persisted between every pair of adjacent timeslices. Although some work has used difference maps for visualization, no user study has been performed to gauge their performance. In this paper, we present a user study to evaluate the effectiveness of difference maps in comparison with presenting the evolution of the dynamic graph over time on three interfaces. We found evidence that difference maps produced significantly fewer errors when determining the number of edges inserted or removed from a graph as it evolves over time. Also, difference maps were significantly preferred on all tasks.

PORGY: Strategy-Driven Interactive Transformation of Graphs

This paper investigates the use of graph rewriting systems as a modelling tool, and advocates the embedding of such systems in an interactive environment. One important application domain is the modelling of biochemical systems, where states are represented by port graphs and the dynamics is driven by rules and strategies. A graph rewriting tools capability to interactively explore the features of the rewriting system provides useful insights into possible behaviours of the model and its properties. We describe PORGY, a visual and interactive tool we have developed to model complex systems using port graphs and port graph rewrite rules guided by strategies, and to navigate in the derivation history. We demonstrate via examples some functionalities provided by PORGY.

The readability of path-preserving clusterings of graphs

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.

PORGY: A Visual Graph Rewriting Environment for Complex Systems

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.

A Visual Analytics Approach to Compare Propagation Models in Social Networks

Numerous propagation models describing social influence in social networks can be found in the literature. This makes the choice of an appropriate model in a given situation difficult. Selecting the most relevant model requires the ability to objectively compare them. This comparison can only be made at the cost of describing models based on a common formalism and yet independent from them. We propose to use graph rewriting to formally describe propagation mechanisms as local transformation rules applied according to a strategy. This approach makes sense when it is supported by a visual analytics framework dedicated to graph rewriting. The paper first presents our methodology to describe some propagation models as a graph rewriting problem. Then, we illustrate how our visual analytics framework allows to interactively manipulate models, and underline their differences based on measures computed on simulation traces.

Strategic Port Graph Rewriting: An Interactive Modelling and Analysis Framework

We present strategic portgraph rewriting as a basis for the implementation of visual modelling and analysis tools. The goal is to facilitate the specification, analysis and simulation of complex systems, using port graphs. A system is represented by an initial graph and a collection of graph rewriting rules, together with a user-defined strategy to control the application of rules. The strategy language includes constructs to deal with graph traversal and management of rewriting positions in the graph. We give a small-step operational semantics for the language, and describe its implementation in the graph transformation and visualisation tool PORGY.

Visual Modelling of Complex Systems: Towards an Abstract Machine for PORGY

PORGY is a visual modelling tool, where a system is defined by a strategic graph program. In this paper, we provide an operational semantics for strategic graph programs by means of an abstract machine. The semantics specifies the valid transformation steps, providing a link between the model and its implementation in PORGY.

GVSR: an on-line guide for choosing a graph visualization software

It is easy to find graph visualization applications for all sorts of uses. However, choosing an appropriate application may be difficult. This poster presents a website (http://gvsr.polytech.univ-nantes.fr/) built to help users to choose a program adapted to their problems. So far, this site references eighty programs and aims at helping users both in their choices and in comparing the programs. The site is also designed as a tool repository helping the community to access and compare the available tools, and benchmark new techniques and algorithms.

Labelled Graph Strategic Rewriting for Social Networks

We develop an algebraic approach, based on labelled-graph strategic rewriting , for the study of social networks, specifically network generation and propagation mechanisms. This approach sheds a new light on these problems, and leads to new or improved generation and propagation algorithms. We argue that relevant concepts are provided by three ingredients: labelled graphs to represent networks of data or users, rewrite rules to describe concurrent local transformations, and strategies to express control. We show how these techniques can be used to generate random networks that are suitable for social network analysis, simulate different propagation mechanisms, and analyse and compare propagation models by extracting common rules and differences, thus leading to improved algorithms. We illustrate with examples the flexibility of the approach.