LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Linhares, Cláudio D. G.; Ponciano, Jean R.; Pedro, Diogenes S.; Rocha, Luis E. C.; Traina, Agma J. M.; Poco, Jorge

doi:10.1109/tvcg.2022.3209477

Cited by 5 publications

(5 citation statements)

References 72 publications

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“…Workarounds: Before discussing workarounds, we would like to make clarification on the definition of "large" in our context. There are different definitions for "large-scale", ranging from 50 nodes [56] to thousands of nodes [57] and even to millions of nodes [58]. These variations account for differences in the domain, the data itself, tasks and visual techniques [56].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Wang,

Chen,

Zhou

et al. 2023

Applied Sciences

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“…To do that, we took advantage of a dataset with market basket information that we modeled as temporal networks focused on purchases of products from the perspective of three distinct groups of household customers, characterized by their annual income. Using LargeNetVis [Linhares et al 2023], an interactive system with layouts and features to analyze small and large temporal networks, we then performed a series of exploratory analyses that allowed us to inspect structure (identifying, e.g., purchase patterns and relations between items and users' preferences) and temporal dynamics (e.g., purchases trends and seasonalities).…”

Section: Discussionmentioning

confidence: 99%

“…We have considered different interactive systems for visualizing temporal networks (e.g., DyNetVis [Linhares et al 2020] and PaohVis [Valdivia et al 2021]). After an initial investigation, we chose LargeNetVis [Linhares et al 2023] because it enables effective analyses of temporal aspects and also can handle temporal networks with a few thousand nodes and edges, such as the ones described in Tab. 1, which would be difficult or infeasible with other systems.…”

Section: Visual Analysismentioning

confidence: 99%

“…To investigate that, we explore market baskets using Network Visualization techniques and inspect the social network structure (e.g., the relation between products bought by customers) and the temporal evolution (e.g., trends and seasonality factors). We take advantage of an established system focused on the visual analysis of large temporal networks called LargeNetVis [Linhares et al 2023], which is suitable for exploring patterns and anomalies using network communities. To detail our approach, we also took advantage of a dataset focused on the temporal network aspects and three household income levels.…”

Section: Introductionmentioning

confidence: 99%

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Exploratory Analysis on Market Basket Data using Network Visualization

Gino,

Pedro,

Ponciano

et al. 2023

Anais Do XII Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2023)

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Market basket analysis is a powerful technique for understanding customer behavior and optimizing business strategies based on that understanding. Market-based analysis over time using visualization techniques can provide insights into market trends and relations, simplify complex data, and communicate insights effectively, which can help organizations make more informed decisions. This paper leverages a dataset focused on the users’ incomes and temporal aspects of market purchases. We modeled this dataset as three distinct temporal networks and performed an exploratory evaluation identifying patterns and anomalies in the data. More specifically, we identified groups of related products, indicating thematic purchases, and evaluated the impact of demographic factors, such as income, on customer spending.

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“…An important question in the context of an experiment like ours is how well the results may scale to larger or more complex networks. There is no clear definition of the number of nodes above which a network is considered to be large (see, e.g., [89]). While for some applications, large networks may refer to thousands or even millions of nodes, Yoghourdjian et al surveyed empirical network visualisation studies and found that such sizes were underrepresented among these studies.…”

Section: Limitationsmentioning

confidence: 99%

De-Emphasise, Aggregate, and Hide: A Study of Interactive Visual Transformations for Group Structures in Network Visualisations

Aichem,

Klein,

Kobourov

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Analysts often have to work with and make sense of large complex networks. One possible solution is to make visualisations interactive, providing users with a way to control visual clutter. Although several interactive methods have been proposed, there may be situations where some of them are too specific to be directly applicable. We have therefore identified several underlying low-level visual transformations, steered by group structures in the networks, and investigated their individual effects on user performance. This may both facilitate the development of further methods and support the generation of new hypotheses. We conducted an exploratory online experiment with 300 participants, involving five tasks, one control condition, and five group-based visual transformations: de-emphasising groups by opacity, position or size, aggregating groups, and hiding groups. The results for the three tasks that were specifically referring to groups show a high usage of the visual transformations by participants and several positive effects of the latter on accuracy, completion time, and mental effort spent. On the other hand, the two tasks that were not directly referring to groups show a lower usage of the visual transformations and the results regarding effects are rather mixed. Supplemental materials are available on DaRUS at https://doi.org/10.18419/darus-3706.

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LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Cited by 5 publications

References 72 publications

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Exploratory Analysis on Market Basket Data using Network Visualization

De-Emphasise, Aggregate, and Hide: A Study of Interactive Visual Transformations for Group Structures in Network Visualisations

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