VENLO: Interactive Visual Exploration of Aligned Biological Networks and Their Evolution

Brasch, Steffen; Fuellen, Georg; Linsen, Lars

doi:10.1007/978-3-642-21608-4_13

Cited by 4 publications

(3 citation statements)

References 34 publications

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“…One can also create a "all-in-one" graph that is the union of all individual graphs and use coloring or annotations to facilitate the reconstruction of the individual graphs. [15] propose a mixture of the above two approaches (2.5D layouts). Here, the correspondence of aligned nodes is implied by drawing all 2D layouts simultaneously.…”

Section: Interpreting the Results: Comparing Graphsmentioning

confidence: 99%

Comparative Process Mining in Education: An Approach Based on Process Cubes

Aalst

Guo

Gorissen

2015

Lecture Notes in Business Information Processing

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Process mining techniques enable the analysis of a wide variety of processes using event data. For example, event logs can be used to automatically learn a process model (e.g., a Petri net or BPMN model). Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process mining techniques are available and their value has been proven in many case studies. However, existing techniques focus on the analysis of a single process rather than the comparison of different processes. In this paper, we propose comparative process mining using process cubes. An event has attributes referring to the dimensions of the process cube. Through slicing, dicing, rolling-up, and drilling-down we can view event data from different angles and produce process mining results that can be compared. To illustrate the process cube concept, we focus on educational data. In particular, we analyze data of students watching video lectures given by the first author. The dimensions of the process cube allow us to compare the process of students that passed the course versus the process of students that failed. We can also analyze differences between male and female students, between different parts of the course, and between Dutch students and international students. The initial analysis provided in this paper is used to elicit requirements for better tool support facilitating comparative process mining.

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Section: Interpreting the Results: Comparing Graphsmentioning

confidence: 99%

Comparative Process Mining in Education: An Approach Based on Process Cubes

Aalst

Guo

Gorissen

2015

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

show abstract

“…Others : There are various further applications of dynamic graphs and their visualization. In research, for instance, in context of biology, evolving metabolic pathways [RUK*10], simulated chemical reaction networks [JSS*12] and uncertainties therein [VHK*13], or protein interaction networks [BFL12] are studied. Psychology and user interface research may profit from depicting eye gaze data as dynamic graphs recorded in eye‐tracking studies [BBR*14, HEF*13].…”

Section: Applicationmentioning

confidence: 99%

A Taxonomy and Survey of Dynamic Graph Visualization

Beck

Burch

Diehl

et al. 2016

Computer Graphics Forum

283

249

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Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node-link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline-based ones. A bibliographic analysis provides insights into the organization and developmentof the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.

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“…A network is the combination of a set of entities and the relationships between them [1]. Network visualization provides a visual means for analyzing the relation data and finds wide-ranging applications across multiple domains [2], spanning from transportation systems [3], social networks [4], biological networks [5,6], contact tracings [7], to academic writing analysis [8]. By nature, a significant portion of these networks is dynamic, where nodes and edges evolve over time.…”

Section: Introductionmentioning

confidence: 99%

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Wang,

Chen,

Zhou

et al. 2023

Applied Sciences

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Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based research primarily focused on stabilizing the layout by defining constraints on local node movement. However, these constraints often neglect the structural influence and its corresponding global impact, which may lead to that the representations of the network structure change significantly over time and a decrease in layout quality. To address this problem, we introduce the Structure-based Influence Propagation and Aging (SIPA) algorithm, a novel approach to preserve the stability of relative node positions and shapes of interconnected nodes (referred to as structures) between adjacent time steps. These stable structures serve as visual cues for users tracking the evolution of the network, thereby enhancing the overall layout stability. Additionally, we enhance dynamic network analysis by a highly interactive visualization system, enriching the layout result with multiple coordinated views of temporal trends, network features, animated graph diaries and snapshots. Our approach empowers users to interactively track and compare network evolution within a long-term temporal context and across multiple aspects. We demonstrate the effectiveness and performance of our approach through in-lab user studies and comparative experiments with three baseline dynamic network layout methods.

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VENLO: Interactive Visual Exploration of Aligned Biological Networks and Their Evolution

Cited by 4 publications

References 34 publications

Comparative Process Mining in Education: An Approach Based on Process Cubes

Comparative Process Mining in Education: An Approach Based on Process Cubes

A Taxonomy and Survey of Dynamic Graph Visualization

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

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