Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables to discover similar temporal summaries (e.g., recurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

ModelSpeX : Model Specification Using Explainable Artificial Intelligence Methods

Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract human-centered rule-sets to generate model specifications from black-box models (e.g., neural networks). The workflow enables to reason about the underlying problem, to extract decision rule sets, and to evaluate the suitability of the model for a particular task. An exemplary usage scenario walks an analyst trough the steps of the workflow to show the applicability.publishe

TECHNOLOGY INTELLIGENCE PROCESS IN TECHNOPARK FIRMS:AN EMPIRICAL RESEARCH IN TURKEY

The new age is characterized by a knowledge based economy and rapidtechnological changes. In this complex environment, technology intelligence (TI)74INTERNATIONAL JOURNAL OF ECONOMICS AND FINANCE STUDIESVol8, No 1, 2016 ISSN:1309-8055 (Online)and forecasting the future technologies areimportantfor being proactive againstthe rapid technological changesand generatingnew innovations. TI is a vitalprocess to sustainthefirm`s competitiveness. This paper examines the technologyintelligence process in scope of Turkish technopark firms. The empirical data wasobtained from 136 technopark firms and evaluated by the context of TI modelsynthesized by the researchers based on the literature. According to the resultsitis determined that, TI mechanisms of the technopark firms are unstructured andtechnoparkfirm’sdon’tallocate enough resources for TI, theyuse theenvironments that can be easily reached such as internet andperiodicalmagazines, andanalyze the data about technology by using heuristicmethodsmostly. This could be related tocharacteristics of the technopark firms such asbeing micro size and owner-manager, insufficientpersonnel and financial sourcesetc. Because in developing countries like Turkey most of the firms are SMEs, afurther research might be useful to develop a specialized TI model for SME

G-Rap: interactive text synthesis using recurrent neural network suggestions

Finding the best neural network configuration for a given goal can be challenging, especially when it is not possible to assess the output quality of a network automatically. We present G-Rap, an interactive interface based on Visual Analytics principles for comparing outputs of multiple RNNs for the same training data. G-Rap enables an iterative result generation process that allows a user to evaluate the outputs with contextual statistics.publishe

dg2pix : Pixel-Based Visual Analysis of Dynamic Graphs

Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix, a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end.publishe

VulnEx : Exploring Open-Source Software Vulnerabilities in Large Development Organizations to Understand Risk Exposure

The prevalent usage of open-source software (OSS) has led to an increased interest in resolving potential third-party security risks by fixing common vulnerabilities and exposures (CVEs). However, even with automated code analysis tools in place, security analysts often lack the means to obtain an overview of vulnerable OSS reuse in large software organizations. In this design study, we propose VulnEx (Vulnerability Explorer), a tool to audit entire software development organizations. We introduce three complementary table-based representations to identify and assess vulnerability exposures due to OSS, which we designed in collaboration with security analysts. The presented tool allows examining problematic projects and applications (repositories), third-party libraries, and vulnerabilities across a software organization. We show the applicability of our tool through a use case and preliminary expert feedback.publishe

Time Series Projection to Highlight Trends and Outliers

The goal of the VAST Challenge 2018 Mini Challenge 2 (MC 2) was to unveil the possible causes and effects of environmental pollution in the Boonsong Lekagul Wildlife Preserve. We propose the ViCCEx (Visual Chemical Contamination Explorer) system that enables to interactively explore the sparse multivariate river network sensor reading dataset to identify characteristics, trends, and outliers of the different sensor reading locations over time. The ViCCEx system uses a t-SNE projection to display an overview visualization, a sampling strategy view to highlight the overall sampling strategies of different chemical measurements at each sensor location, and various extracted statistics to highlight the evolution of chemical measurements. The three views are connected via linking and brushing, which enables to explore and identify possible pollution causes and effects in the preserve.publishe