visone - Software for the Analysis and Visualization of Social Networks

We present the software tool visone which combines graph-theoretic methods for the analysis of social networks with tailored means of visualization. Our main contribution is the design of novel graph-layout algorithms which accurately reflect computed analyses results in well-arranged drawings of the networks under consideration. Besides this, we give a detailed description of the design of the software tool and the provided analysis methods

Measuring and improving the readability of network visualizations

Network data structures have been used extensively for modeling entities and their ties across such diverse disciplines as Computer Science, Sociology, Bioinformatics, Urban Planning, and Archeology. Analyzing networks involves understanding the complex relationships between entities as well as any attributes, statistics, or groupings associated with them. The widely used node-link visualization excels at showing the topology, attributes, and groupings simultaneously. However, many existing node-link visualizations are difficult to extract meaning from because of (1) the inherent complexity of the relationships, (2) the number of items designers try to render in limited screen space, and (3) for every network there are many potential unintelligible or even misleading visualizations. Automated layout algorithms have helped, but frequently generate ineffective visualizations even when used by expert analysts. Past work, including my own described herein, have shown there can be vast improvements in network visualizations, but no one can yet produce readable and meaningful visualizations for all networks. Since there is no single way to visualize all networks effectively, in this dissertation I investigate three complimentary strategies. First, I introduce a technique called motif simplification that leverages the repeating patterns or motifs in a network to reduce visual complexity. I replace common, high-payoff motifs with easily understandable glyphs that require less screen space, can reveal otherwise hidden relationships, and improve user performance on many network analysis tasks. Next, I present new Group-in-a-Box layouts that subdivide large, dense networks using attribute- or topology-based groupings. These layouts take group membership into account to more clearly show the ties within groups as well as the aggregate relationships between groups. Finally, I develop a set of readability metrics to measure visualization effectiveness and localize areas needing improvement. I detail optimization recommendations for specific user tasks, in addition to leveraging the readability metrics in a user-assisted layout optimization technique. This dissertation contributes an understanding of why some node-link visualizations are difficult to read, what measures of readability could help guide designers and users, and several promising strategies for improving readability which demonstrate that progress is possible. This work also opens several avenues of research, both technical and in user education

Persistent Homology Guided Force-Directed Graph Layouts

Graphs are commonly used to encode relationships among entities, yet their abstractness makes them difficult to analyze. Node-link diagrams are popular for drawing graphs, and force-directed layouts provide a flexible method for node arrangements that use local relationships in an attempt to reveal the global shape of the graph. However, clutter and overlap of unrelated structures can lead to confusing graph visualizations. This paper leverages the persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout. In particular, the user adds and removes contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Finally, we demonstrate the utility of our approach across a variety of synthetic and real datasets

Development of Visualization Tools for Dynamic Networks and Evaluation of Visual Stability Characteristics

Das dynamische Graphenzeichnen ist das Mittel der Wahl, wenn es um die Analyse und Visualisierung dynamischer Netzwerke geht. Diese Zeichnungen werden oft durch Festhalten aufeinanderfolgender Datenreihen oder “Snapshots” des untersuchten Netzwerkes erzeugt. Für jede von diesen Zeichnungen wird mit Hilfe des ausgewählten Algorithmus eine unabhängige Graphenzeichnung berechnet und die resultierende Sequenz wird dem Benutzer in einer vorbestimmten Reihenfolge präsentiert. Trotz der Einfachheit dieser Methode tauchen bei der dynamischen Graphenzeichnung mit der vorher genannten Strategie Probleme auf. Teilnehmer, Verbindungen und Muster können während der Untersuchung des dynamischen Netzwerkes ihre Position auf der Darstellung verändern. Außerdem neigen dynamische Graphenzeichnungen dazu, fortlaufend Elemente ohne vorhergehende Information hinzuzufügen und zu entfernen. Als Konsequenz ergibt sich die Schwierigkeit, die Entwicklung der Mitglieder des Netzwerkes zu beobachten. Es wurden verschiedene Techniken zur Anpassung von Layouts entwickelt, welche das Ziel haben, die Änderungen der dynamischen Graphenzeichnung zu minimieren. Einige von ihnen schlagen vor, dass die Grundstruktur der Zeichnung jederzeit beibehalten werden muss. Andere wiederum, dass jeder Teilnehmer und jede Beziehung einer fixen Position im Euklidischen Raum zugeordnet werden soll. Eine neu entwickelte Technik schlägt eine Alternative vor: Mehrere Teilnehmer können gleichzeitig einen Knotenpunkt im Euklidischen Raum beanspruchen, solange sie nicht zum selben Zeitpunkt erscheinen. Mehrere Beziehungen können unter den vorgenannten Bedingungen dementsprechend denselben Eckpunkt im Euklidischen Raum beanspruchen. Daraus folgt, dass die dynamische Graphenzeichnung ihre Veränderungen minimiert bis hin zu einem Zustand, in dem es als "visuell stabil" angesehen werden kann. Diese Arbeit zeigt inwieweit die visuelle Stabilität einer dynamischen Graphenzeichnung die Benutzererfahrung und die Effektivität der visuellen Suche beim Verfolgen der Mitglieder oder Netzwerkeigenschaften beeinflusst. Zu diesem Zweck wurde ein Framework zur Unterstützung flexibler Visualisierungstechniken entwickelt. Es diente als Plattform, um existierende Techniken zu bewerten. Solche Bewertungen kombinieren den Gebrauch von Fragebögen, um Informationen über die Nutzererfahrung zu sammeln, ein Eye-Tracking System, um die Augenbewegungen zu erfassen sowie ein neues mathematisches Modell zur Quantifizierung der visuellen Stabilität einer dynamischen Graphenzeichnung. Die daraus folgenden Resultate ergeben, dass es einen Zielkonflikt zwischen der Benutzererfahrung und der Effizienz der visuellen Suche gibt, welche von der visuellen Stabilität der dynamischen Graphenzeichnung abhängt. Einerseits bieten dynamische Graphenzeichnungen mit einem höheren Niveau an visueller Stabilität eine bessere Benutzererfahrung bei Verfolgungsaufgaben, aber eine schlechtere Effizienz bei der visuellen Suche. Andererseits bieten dynamische Graphenzeichnungen mit einer geringeren visuellen Stabilität eine nicht zufriedenstellende Benutzererfahrung, jedoch im Austausch eine Verbesserung der Effizienz der visuellen Suche. Dieses Ergebnis wird genutzt, um visuell stabile Beschreibungen zu entwickeln, die darauf abzielen, die Netzwerkeigenschaften über einen gewissen Zeitraum zu untersuchen. Solche Beschreibungen und Empfehlungen bedienen sich Merkmalen wie Skalierung und Hervorhebung, um die Effizienz der visuellen Suche zu verbessern.Dynamic graph drawings are the metaphor of choice when it comes to the analysis and visualization of dynamic networks. These drawings are often created by capturing a successive sequence of states or “snapshots” from the network under study. Then, for each one of them, a graph drawing is independently computed with the layout algorithm of preference and the resulting sequence is presented to the user in a predefined order. Despite the simplicity of the method, dynamic graph drawings created with the pre- vious strategy possess some problems. Actors, relations or patterns can change their position on the canvas as the dynamic network is explored. Furthermore, dynamic graph drawings tend to constantly add and remove elements without prior information. As a consequence, it is very difficult to observe how the members of the network evolve over time. The scientific community has developed a series of layout adjustment techniques, which aim at minimizing the changes in a dynamic graph drawing. Some of them suggest that the “shape” of the drawing must be maintained at all time. Others that every actor and relation must be assigned to a fixed position in the Euclidean Space. However, a recently developed technique proposes an alternative. Multiple actors can occupy the same node position in the Euclidean Space, as long as they do not appear at the same point in time. Likewise, multiple relations can occupy the same edge position in the Euclidean Space following the principle aforementioned. As the result, a dynamic graph drawing minimizes its changes to a point where it can be perceived as visually stable. This thesis presents how the visual stability of a dynamic graph drawing affects the user experience and the efficiency of the visual search when tracking actors or network attributes over time. For this purpose, a framework to support flexible visualization techniques was developed. It served as the platform to evaluate existing layout ad- justment techniques. Such an evaluation combined the use of questionnaires to gather information about the user experience; an eye-tracking device to record the eye move- ments and a new mathematical model to appropriately quantify the visual stability of dynamic graph drawings. The results obtained suggest that there is a trade-off between the user experience and the efficiency of the visual search, which depends on the visual stability of a dynamic graph drawing. On the one hand, dynamic graph drawings with higher levels of visual stability provide a satisfying user experience in tracking tasks. Nonetheless, they are inefficient in terms of the visual search. On the other hand, dynamic graph drawings with lower levels of visual stability, do not provide a satisfying user experience in tracking tasks but considerably improve the efficiency of the visual search. These findings were used to develop visually stable metaphors, aiming at exploring network attributes over time. Such metaphors rely on features like scaling or highlighting to improve the efficiency of the visual search