Timeslices are often used to draw and visualize dynamic graphs. While timeslices are a natural way to think about dynamic graphs, they are routinely imposed on continuous data. Often, it is unclear how many timeslices to select: too few timeslices can miss temporal features such as causality or even graph structure while too many timeslices slows the drawing computation. We present a model for dynamic graphs which is not based on timeslices, and a dynamic graph drawing algorithm, DynNoSlice, to draw graphs in this model. In our evaluation, we demonstrate the advantages of this approach over timeslicing on continuous data sets.
In this paper we consider the problem of drawing and displaying a series of related graphs, i.e., graphs that share all, or parts of the same vertex set. We designed and implemented three different algorithms for simultaneous graph drawing and three different visualization schemes. The algorithms are based on a modification of the force-directed algorithm that allows us to take into account vertex weights and edge weights in order to achieve mental map preservation while obtaining individually readable drawings. The implementation is in Java and the system can be downloaded at http: simg.cs.arizona.edu .  GraphAEL extracts three types of evolving graphs from the Graph Drawing literature and creates 2D and 3D animations of the evolutions. We study citation graphs, topic graphs, and collaboration graphs. We also create difference graphs which capture the nature of change between two given time periods. GraphAEL can be accessed online at http: graphael.cs.arizona.edu. Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections. The graphael system implements several traditional force-directed layout methods, as well as several novel layout methods for non-Euclidean geometries, including hyperbolic and spherical. The system can handle large graphs, using multi-scale variations of the force-directed methods. Moreover, graphael can layout and visualize graphs that evolve though time, using static views, animation, and morphing. The implementation includes a powerful interface that allows the user to put together existing algorithms and visualization techniques, and to easily add new ones. The system is written in Java and is available as a downloadable program or as an applet at http: graphael.cs.arizona.edu. Social media such as blogs and microblogs enable users to easily and rapidly publish information on their personal activities and interests. They are considered to provide valuable information from the viewpoints of sociology, linguistics, and marketing. This paper proposes a novel system for analyzing temporal changes in the activities and interests of bloggers through a 3D visualization of phrase dependency structures in sentences. We first extract events that represent bloggers' activities and interests through analyzing the phrase dependencies of sentences in a blog archive. We roughly categorize the retrieved events according to the thematic roles (such as the experiencer, agent, and location) of the noun within the events, and then store them in a dependency database so that we can retrieve events that involve a given topic. Second, we present a 3D visualization framework for exploring temporal changes in events related to a topic. Our framework enables users to find events about a topic that appear within a specific timing, and drill down details of the events. It also enables users to compare events with different timings and or on multiple topics. Moreover, it allows them to observe an overview of temporal changes in sets of events, and long-term changes in the frequency of events to assist users in finding trends. We implement the proposed system on our own five-year blog archive that focused on Japanese, and we report the usefulness of our system by using various examples. In dynamic graph drawing, the input is a sequence of graphs for which a sequence of layouts is to be generated such that the quality of individual layouts is balanced with layout stability over time. Qualitatively different extensions of drawing algorithms for static graphs to the dynamic case have been proposed, but little is known about their relative utility. We report on a quantitative study comparing the three prototypical extensions via their adaptation for the stress-minimization framework. While some findings are more subtle, the linking approach connecting consecutive instances of the same vertex is found to be the overall method of choice.