TimeLighting: Guidance-enhanced Exploration of 2D Projections of Temporal Graphs

In temporal (or event-based) networks, time is a continuous axis, with real-valued time coordinates for each node and edge. Computing a layout for such graphs means embedding the node trajectories and edge surfaces over time in a 2D + t space, known as the space-time cube. Currently, these space-time cube layouts are visualized through animation or by slicing the cube at regular intervals. However, both techniques present problems ranging from sub-par performance on some tasks to loss of precision. In this paper, we present TimeLighting, a novel visual analytics approach to visualize and explore temporal graphs embedded in the space-time cube. Our interactive approach highlights the node trajectories and their mobility over time, visualizes node "aging", and provides guidance to support users during exploration. We evaluate our approach through two case studies, showing the system's efficacy in identifying temporal patterns and the role of the guidance features in the exploration process.Comment: Appears in the Proceedings of the 31st International Symposium on Graph Drawing and Network Visualization (GD 2023

Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation

Analysis of large dynamic networks is a thriving research field, typically relying on 2D graph representations. The advent of affordable head mounted displays however, sparked new interest in the potential of 3D visualization for immersive network analytics. Nevertheless, most solutions do not scale well with the number of nodes and edges and rely on conventional fly- or walk-through navigation. In this paper, we present a novel approach for the exploration of large dynamic graphs in virtual reality that interweaves two navigation metaphors: overview exploration and immersive detail analysis. We thereby use the potential of state-of-the-art VR headsets, coupled with a web-based 3D rendering engine that supports heterogeneous input modalities to enable ad-hoc immersive network analytics. We validate our approach through a performance evaluation and a case study with experts analyzing a co-morbidity network

A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual Analytics

Guidance can support users during the exploration and analysis of complex data. Previous research focused on characterizing the theoretical aspects of guidance in visual analytics and implementing guidance in different scenarios. However, the evaluation of guidance-enhanced visual analytics solutions remains an open research question. We tackle this question by introducing and validating a practical evaluation methodology for guidance in visual analytics. We identify eight quality criteria to be fulfilled and collect expert feedback on their validity. To facilitate actual evaluation studies, we derive two sets of heuristics. The first set targets heuristic evaluations conducted by expert evaluators. The second set facilitates end-user studies where participants actually use a guidance-enhanced system. By following such a dual approach, the different quality criteria of guidance can be examined from two different perspectives, enhancing the overall value of evaluation studies. To test the practical utility of our methodology, we employ it in two studies to gain insight into the quality of two guidance-enhanced visual analytics solutions, one being a work-in-progress research prototype, and the other being a publicly available visualization recommender system. Based on these two evaluations, we derive good practices for conducting evaluations of guidance in visual analytics and identify pitfalls to be avoided during such studies.Comment: Accepted to IEEE VIS 202

Sabrina: Modeling and Visualization of Economy Data with Incremental Domain Knowledge

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process

Study of production and cold nuclear matter effects in pPb collisions at=5 TeV

Production of mesons in proton-lead collisions at a nucleon-nucleon centre-of-mass energy = 5 TeV is studied with the LHCb detector. The analysis is based on a data sample corresponding to an integrated luminosity of 1.6 nb(-1). The mesons of transverse momenta up to 15 GeV/c are reconstructed in the dimuon decay mode. The rapidity coverage in the centre-of-mass system is 1.5 < y < 4.0 (forward region) and -5.0 < y < -2.5 (backward region). The forward-backward production ratio and the nuclear modification factor for (1S) mesons are determined. The data are compatible with the predictions for a suppression of (1S) production with respect to proton-proton collisions in the forward region, and an enhancement in the backward region. The suppression is found to be smaller than in the case of prompt J/psi mesons

Exploratory User Study on Graph Temporal Encodings

An exploratory user study with the aim of evaluating the users’ perception and the performance of different temporal encodings, such as, superimposition, juxtaposition, and animation, on node-link and adjacency matrix graph representations

A Million Edge Drawing for a Fistful of Dollars

In this paper we study the problem of designing a graph drawing algorithm for large graphs. The algorithm must be simple to implement and the computing infrastructure must not require major hardware or software investments. We report about the experimental analysis of a simple implementation of a spring embedder in Giraph, a vertex-centric open source framework for distributed computing. The algorithm is tested on real graphs of up to 1 million edges by using a cheap PaaS (Platform as a Service) infrastructure of Amazon. We can afford drawing graphs with about one million edges in about 8 min, by spending less than 1 USD per drawing for the cloud computing infrastructure