Daniel Archambault

Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision‐making as well as communication strategies. Visual Analytics has shown significant potential in supporting comprehension of ID processes in several domains. These approaches vary greatly in their design, both in terms of data and visual encodings. The variety of designs and application domains motivates this survey, which complements existing surveys with a focus on visualization of transmission processes in contrast to other surveys about visualizing networks. This survey defines types of transmission and medium networks, identifies common visualization principles, categorizes them, and identifies gaps–opportunities for future research.

Edge bundling algorithms can significantly improve the visualization of dense graphs by identifying and bundling together suitable groups of edges and thus reducing visual clutter. As such, bundling is often viewed as a post-processing step applied to a drawing, and the vast majority of edge bundling algorithms consider a graph and its drawing as input. A different way of thinking about edge bundling is to simultaneously optimize both the drawing and the bundling, which we investigate in this paper. We build on an earlier work where we introduced a novel algorithmic framework for bundling-aware graph drawing consisting of three main steps, namely Filter for a skeleton subgraph, Draw the skeleton, and Bundle the remaining edges against the drawing of the skeleton. We propose several alternative implementations and experimentally compare them against each other and the simple idea of first drawing the full graph and subsequently applying edge bundling to it. The experiments confirm that bundled drawings created by our Filter-Draw-Bundle framework outperform previous approaches according to metrics for edge bundling and graph drawing.

The pandemic had broad reaching impacts on how we do many things, including the way that we design and implement visualizations. In this article, we reflect on how visualization design changed in an emergency response. Based on these reflections, we present modifications to design methodologies for visualizations to accommodate an emergency response and its working conditions.

Machine learning in financial Time Series Forecasting (TSF) has a clear application in investment, where predicting stock price movements can inform investment strategies. The Transformer model has emerged as a powerful tool for this purpose, yet significant research gaps remain. Existing studies often focus on a set of stocks without modeling stock behavior within specific sectors. Notably, there is a lack of research on the Consumer Cyclicals (CC) industry, which includes sectors such as automotive, housing, entertainment, and retail. These industries are highly sensitive to economic conditions, making them crucial for understanding broader economic impacts on stock behavior. Furthermore, we observe that many existing works neglect broader economic contexts, which is particularly important for CC analysis due to its sensitivity to economic trends. Additionally, previous studies on financial Transformer models typically use model tokens as feature vectors of multiple variates at a single timestep. This approach may not adequately capture the important relationships between dataset variates for long-term economic trends. To this end, we present the first study on financial TSF for the CC sector along with economic data. To support the research, we propose the first public benchmark dataset for the CC sector, consisting of traditional stock price time series, technical indicators, and temporal data, enriched with economic indicators. Next, we introduce an alternative tokenization approach to enhance the model’s ability to capture long-term trends by preserving information about nonlinear dependencies between dataset variates. We hypothesize that this approach helps capture long-term signals more effectively. Through a comprehensive data ablation study and benchmark testing, we demonstrate that incorporating economic indicators improves the accuracy of longer-term predictions for the CC sector, and the new tokenization method enhances the performance of Transformer models. The dataset and code are made publicly available at: https://github.com/KasperKrawczyk/econtrans_dataset

In temporal ( 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 such as below-average performance on tasks as well as loss of precision and difficulties in selecting timeslice intervals. 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 node trajectories and their movement over time, visualizes node “aging”, and provides guidance to support users during exploration by indicating interesting time intervals (“when”) and network elements (“where”) are located for a detail-oriented investigation. This combined focus helps to gain deeper insights into the temporal network's underlying behavior. We assess the utility and efficacy of our approach through two case studies and qualitative expert evaluation. The results demonstrate how TimeLighting supports identifying temporal patterns, extracting insights from nodes with high activity, and guiding the exploration and analysis process.

Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.