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

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

Consistent transformations of content-based routing networks in OpenFlow

Content-based Publish/Subscribe-Systeme sind weitverbreitet. Sie bieten die Möglichkeit, Nachrichten schnell und einfach an mehrere Teilnehmer zu verteilen ohne, dass der Sender von den Teilnehmern weiß. Diese Systeme haben allerdings den Nachteil, dass sie auf der Anwendungsebene implementiert sind und damit ein gewisses Maß an Overhead mitnehmen. Mit dem Aufkommen von Software-defined networking hat man nun die Möglichkeit, solche Systeme auf der Netzwerkebene zu implementieren und muss den Overhead der Anwendungsebene nicht mitnehmen. Die Idee zu einem solchen System wurde vorgestellt, allerdings benötigt es dafür unterschiedlichste Komponenten. Eine dieser Komponenten soll in dieser Arbeit vorgestellt werden. Dabei handelt es sich um einen Update-Mechanismus, der die Veränderungen im Netzwerk ohne Nachrichtenverlust und ohne Duplikate umsetzen kann. Veränderungen kommen durch z.B. neue Subscriber oder veränderte Subscriptions. Dies spiegelt sich in einer Veränderungen des Graphen wieder, der das Netzwerk darstellt. Der Update-Mechanismus erkennt Veränderungen zwischen zwei Graphen und sorgt dafür, dass am Ende der aktuelle Graph im Netzwerk implementiert ist

User Interfaces and Difference Visualizations for Alternatives

Designers often create multiple iterations to evaluate alternatives. Todays computer-based tools do not support such easy exploration of a design space, despite the fact that such support has been advocated. This dissertation is centered on this. I begin by investigating the effectiveness of various forms of difference visualizations and support for merging changes within a system targeted at diagrams with node and edge attributes. I evaluated the benefits of the introduced difference visualization techniques in two user studies. I found that the basic side-by-side juxtaposition visualization was not effective and also not well received. For comparing diagrams with matching node positions, participants preferred the side-by-side option with a difference layer. For diagrams with non-matching positions animation was beneficial, but the combination with a difference layer was preferred. Thus, the difference layer technique was useful and a good complement to animation. I continue by investigating if explicit support for design alternatives better supports exploration and creativity in a generative design system. To investigate the new techniques to better support exploration, I built a new system that supports parallel exploration of alternative designs and generation of new structural combinations. I investigate the usefulness of my prototype in two user studies and interviews. The results and feedback suggest and confirm that supporting design alternatives explicitly enables designers to work more creatively. Generative models are often represented as DAGs (directed acyclic graphs) in a dataflow programming environment. Existing approaches to compare such DAGs do not generalize to multiple alternatives. Informed by and building on the first part of my dissertation, I introduce a novel user interface that enables visual differencing and editing alternative graphsspecifically more than two alternatives simultaneously, something that has not been presented before. I also explore multi-monitor support to demonstrate that the difference visualization technique scales well to up to 18 alternatives. The novel jamming space feature makes organizing alternatives on a 23 monitor system easier. To investigate the usability of the new difference visualization method I conducted an exploratory interview with three expert designers. The received comments confirmed that it meets their design goals

Visualisations novatrices pour la compréhension de réseaux et de logiciels complexes

La visualisation d’information a le potentiel de pouvoir exploiter nos capacités visuelles, acquises au fil de centaines de millions d’années d’évolution, afin de faciliter la découverte de secrets enfouis dans les données, de nouveaux patrons ou de relations insoupçonnées. Il existe toutefois une grande variété de données, plus ou moins structurées, que l’on cherche à comprendre sous diverses perspectives. En particulier, les données sous forme de réseaux servent à modéliser des phénomènes importants, tels que les communautés sociales ou les transactions financières, mais peuvent être difficiles à représenter si les réseaux sont grands, hiérarchiques, et/ou dynamiques. Cette thèse se concentre sur la conception de nouvelles techniques de visualisation de réseaux, dans le but de faciliter la compréhension de données. Les techniques de visualisation présentes dans la littérature sont utiles dans certains contextes et comportent chacune des limitations. Néanmoins, il existe encore des possibilités inexplorées pour créer des nouvelles façons de représenter des données. La validation de ces nouvelles techniques demeure un défi. En outre, les interfaces doivent être simples à utiliser, mais aussi faciliter l’analyse et l’exploration de données. Dans le but d’étudier de nouvelles options de visualisations pour faciliter des tâches de compréhension des données, nous avons d’abord classifié les travaux antérieurs avec des taxonomies. De cette manière, nous avons aussi pu mettre en lumière des nouvelles pistes d’hybrides (c’est-à-dire, des combinaisons d’approches) potentiellement intéressantes pour visualiser des réseaux statiques et dynamiques. Les contributions présentées dans cette thèse couvrent différents aspects de la visualisation de réseaux complexes et dynamiques. D’abord, le premier chapitre se concentre sur la visualisation de réseaux statiques comportant des hiérarchies, par la combinaison d’approches. Le prototype décrit dans le deuxième chapitre permet également de combiner des représentations visuelles, mais peut être aussi utilisé afin de modéliser des graphes dynamiques. Enfin, le troisième chapitre présente une nouvelle méthode visuelle appliquée afin de tracer l’évolution de structures de conception complexes dans des logiciels (modélisés par des réseaux). Ainsi, dans le premier prototype (TreeMatrix), des parties de graphes sont montrées avec des matrices et des diagrammes noeuds-liens, alors que les arborescences sont représentées par des diagrammes en glaçons et des regroupements. Contrairement aux autres visualisations dans la littérature, cette nouvelle technique aide à montrer des réseaux denses, sans nuire à la compréhension des liens à plus haut niveau. Une expérience avec des utilisateurs a montré certains avantages afin de découvrir et organiser les liens de modules au sein d’un logiciel, en comparaison avec le logiciel commercial Lattix. Nous avons également combiné des approches de manière novatrice pour notre second prototype (DiffAni) afin de visualiser des réseaux qui évoluent dans le temps. DiffAni est le premier hybride interactif de graphes dynamiques et sa validation avec des participants a permis de faire ressortir certains avantages. Ainsi, l’utilisation d’animation doit être modérée et est surtout utile lors de mouvements significatifs. Ces résultats, avec nos taxonomies, pourraient contribuer à guider la création de nouveaux hybrides dans le futur. Le troisième prototype (IHVis) a facilité l’exploration et le traçage de structures de conception dans des logiciels en évolution (modélisés par des réseaux) à partir de répertoires de code source. Cette nouvelle visualisation a notamment révélé des cas d’introduction de points de stabilité et des refactorings, et certains participants ont aussi trouvé d’autres informations intéressantes, telles que l’extension de fonctionnalités par l’implémentation d’interfaces. En résumé, cette thèse présente des façons novatrices et utiles de visualiser des réseaux complexes et dynamiques. Nos principales contributions sont (1) l’exploration d’espaces de conception de nouvelles visualisations de réseaux à l’aide de taxonomies, (2) la conception de prototypes combinant des approches pour visualiser des réseaux hiérarchiques et dynamiques, (3) la conception d’une nouvelle méthode visuelle d’exploration des variations et des instabilités au sein de logiciels en évolution, (4) l’évaluation de ces techniques à l’aide d’expériences avec des participants

Visualisation Support for Biological Bayesian Network Inference

Extracting valuable information from the visualisation of biological data and turning it into a network model is the main challenge addressed in this thesis. Biological networks are mathematical models that describe biological entities as nodes and their relationships as edges. Because they describe patterns of relationships, networks can show how a biological system works as a whole. However, network inference is a challenging optimisation problem impossible to resolve computationally in polynomial time. Therefore, the computational biologists (i.e. modellers) combine clustering and heuristic search algorithms with their tacit knowledge to infer networks. Visualisation can play an important role in supporting them in their network inference workflow. The main research question is: “How can visualisation support modellers in their workflow to infer networks from biological data?” To answer this question, it was required to collaborate with computational biologists to understand the challenges in their workflow and form research questions. Following the nested model methodology helped to characterise the domain problem, abstract data and tasks, design effective visualisation components and implement efficient algorithms. Those steps correspond to the four levels of the nested model for collaborating with domain experts to design effective visualisations. We found that visualisation can support modellers in three steps of their workflow. (a) To select variables, (b) to infer a consensus network and (c) to incorporate information about its dynamics.To select variables (a), modellers first apply a hierarchical clustering algorithm which produces a dendrogram (i.e. a tree structure). Then they select a similarity threshold (height) to cut the tree so that branches correspond to clusters. However, applying a single-height similarity threshold is not effective for clustering heterogeneous multidimensional data because clusters may exist at different heights. The research question is: Q1 “How to provide visual support for the effective hierarchical clustering of many multidimensional variables?” To answer this question, MLCut, a novel visualisation tool was developed to enable the application of multiple similarity thresholds. Users can interact with a representation of the dendrogram, which is coordinated with a view of the original multidimensional data, select branches of the tree at different heights and explore different clustering scenarios. Using MLCut in two case studies has shown that this method provides transparency in the clustering process and enables the effective allocation of variables into clusters.Selected variables and clusters constitute nodes in the inferred network. In the second step (b), modellers apply heuristic search algorithms which sample a solution space consisting of all possible networks. The result of each execution of the algorithm is a collection of high-scoring Bayesian networks. The task is to guide the heuristic search and help construct a consensus network. However, this is challenging because many network results contain different scores produced by different executions of the algorithm. The research question is: Q2 “How to support the visual analysis of heuristic search results, to infer representative models for biological systems?” BayesPiles, a novel interactive visual analytics tool, was developed and evaluated in three case studies to support modellers explore, combine and compare results, to understand the structure of the solution space and to construct a consensus network.As part of the third step (c), when the biological data contain measurements over time, heuristics can also infer information about the dynamics of the interactions encoded as different types of edges in the inferred networks. However, representing such multivariate networks is a challenging visualisation problem. The research question is: Q3 “How to effectively represent information related to the dynamics of biological systems, encoded in the edges of inferred networks?” To help modellers explore their results and to answer Q3, a human-centred crowdsourcing experiment took place to evaluate the effectiveness of four visual encodings for multiple edge types in matrices. The design of the tested encodings combines three visual variables: position, orientation, and colour. The study showed that orientation outperforms position and that colour is helpful in most tasks. The results informed an extension to the design of BayePiles, which modellers evaluated exploring dynamic Bayesian networks. The feedback of most participants confirmed the results of the crowdsourcing experiment.This thesis focuses on the investigation, design, and application of visualisation approaches for gaining insights from biological data to infer network models. It shows how visualisation can help modellers in their workflow to select variables, to construct representative network models and to explore their different types of interactions, contributing in gaining a better understanding of how biological processes within living organisms work