dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

Cakmak, Eren; Jäckle, Dominik; Schreck, Tobias; Keim, Daniel A.

doi:10.1109/vds51726.2020.00008

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…Recently, clustering algorithms have become popular in visualizing dense networks to reduce screen space constraints and reveal patterns on large networks. For example, Cakmak et al [24] applied a hierarchical density-based spatial clustering algorithm [25] to reduce VC in visualizing long sequences of dynamic graphs. The clustering algorithm grouped and arranged similar features in pixel-based visualizations to reveal the temporal patterns.…”

Section: Related Workmentioning

confidence: 99%

Visual Analytics in Explaining Neural Networks with Neuron Clustering

Alicioglu,

Sun

2024

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Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study presents a compact neural network (NN) view design to reduce the visual clutter in explaining the DL model components for domain experts and end users. We utilized clustering algorithms to group hidden neurons based on their activation similarities. This design supports the overall and detailed view of the neuron clusters. We used a tabular healthcare dataset as a case study. The design for clustered results reduced visual clutter among neuron representations by 54% and connections by 88.7% and helped to observe similar neuron activations learned during the training process.

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Section: Related Workmentioning

confidence: 99%

Visual Analytics in Explaining Neural Networks with Neuron Clustering

Alicioglu,

Sun

2024

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“…Particularly, its backend model first extracts node embeddings of different timestamps and then applies embedding alignment, which shows strong capability in capturing local information, i.e., the similarity among players. Eren et al [11] displayed the similarity and difference of temporally dynamic graphs by sorting the graph embedding vectors. Our approach is different from the above works on two dimensions.…”

Section: Graph Latent Representationsmentioning

confidence: 99%

RoleSeer: Understanding Informal Social Role Changes in MMORPGs via Visual Analytics

Xie

et al. 2022

CHI Conference on Human Factors in Computing Systems

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Massively multiplayer online role-playing games create virtual communities that support heterogeneous "social roles" determined by gameplay interaction behaviors under a specific social context. For all social roles, formal roles are pre-defined, obvious, and explicitly ascribed to the people holding the roles, whereas informal roles are not well-defined and unspoken. Identifying the informal roles and understanding their subtle changes are critical to designing sociability mechanisms. However, it is nontrivial to understand the existence and evolution of such roles due to their loosely defined, interconvertible, and dynamic characteristics. We propose a visual analytics system, RoleSeer, to investigate informal roles from the perspectives of behavioral interactions and depict their dynamic interconversions and transitions. Two cases, experts' feedback, and a user study suggest that RoleSeer helps interpret the identified informal roles and explore the patterns behind role changes. We see our approach's potential in investigating informal roles in a broader range of social games. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Visualization; User studies.

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“…All evaluations are primarily usage scenarios. For instance, dg2pix [42] provides an overview of large dynamic graphs using a dense pixelbased visualization to explore graph embeddings at multiple temporal scales. A notable paper is Pálenik et al [43] that proposes a pixelmap to analyze spatio-temporal particle simulations at multiple temporal and spatial scales.…”

Section: Graph and Tree (8/36)mentioning

confidence: 99%

Multiscale Visualization: A Structured Literature Analysis

Cakmak

Jäckle

Schreck

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this work, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference papers between 1995 and 2020. We organized the reviewed papers in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed papers, we examine research trends and highlight open research challenges.

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dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

Cited by 6 publications

References 44 publications

Visual Analytics in Explaining Neural Networks with Neuron Clustering

Visual Analytics in Explaining Neural Networks with Neuron Clustering

RoleSeer: Understanding Informal Social Role Changes in MMORPGs via Visual Analytics

Multiscale Visualization: A Structured Literature Analysis

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