EOD Edge Sampling for Visualizing Dynamic Network via Massive Sequence View

Zhao, Ying; She, Yanmin; Chen, Wenjiang; Lu, Yutian; Xia, Jiazhi; Chen, Wei; Liu, Junrong; Zhou, Fangfang

doi:10.1109/access.2018.2870684

Cited by 27 publications

(29 citation statements)

References 36 publications

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“…Visualisation techniques have been widely used in recent years to analyze data from various domains, e.g., in healthcare applications (Shneiderman et al, 2013), corporate environments (van den Elzen et al, 2013;Zhao et al, 2018) and social networks (Linhares et al, 2019). In educational context, Information Visualisation strategies can be used to identify patterns and behaviours involving students and teachers (Ruipérez-Valiente et al, 2015;Caligaris et al, 2015;McNeil, 2015), improving and optimizing the teaching and learning process (Vieira et al, 2018).…”

Section: Information Visualisation In Educational Contextmentioning

confidence: 99%

Visual Analysis of Contact Patterns in School Environments

Ponciano¹,

Linhares²,

Melo³

et al. 2020

Informatics in Education

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Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/ teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.

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Section: Information Visualisation In Educational Contextmentioning

confidence: 99%

Visual Analysis of Contact Patterns in School Environments

Ponciano¹,

Linhares²,

Melo³

et al. 2020

Informatics in Education

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“…One way to identify the best techniques for proper tasks is to conduct a user study, where, according to a given a task, the user is capable of choosing what is the best representation in a context. Considering the methods for network visualization, the most recent techniques have limited visual scalability (ELZEN et al, 2014;LINHARES et al, 2017b;BURCH, 2017;VEHLOW et al, 2015;ZHAO et al, 2018), becoming important further investigation in how to adapt or create novel techniques to allow a visualization for large networks.…”

Section: Motivationmentioning

confidence: 99%

“…Another technique on the MSV layout is to sample some edges according to some deĄned measure, as explained previously. In (ZHAO et al, 2018), it is created a sampling of the edges maintaining the edges distribution over time called Edge Overlapping Degree (EOD). The authors use the kernel density estimation to create an edge overlapping degree and characterize temporal features of node pairs, being able to choose which edges are hindering the analysis.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…For the structural layout, the software contains techniques to organize the nodes in the layout, such as fast organic, circular and hierarchical (BAT-TISTA et al, 1994). For the temporal (MSV) layout, DyNetVis offers: node ordering, random, appearance, lexicographic, degree (ELZEN et al, 2013), Recurrent Neighbors (RN) (LINHARES et al, 2017b), and Community-based Node Ordering (CNO) (LIN-HARES et al, 2019b); the Temporal Activity Map (TAM) heatmap (LINHARES et al, 2017b); edge sampling, such as random, Accept-Reject (AR) (ZHAO et al, 2018), and Edge Overlapping Degree (EOD) (ZHAO et al, 2018). The matrix layout also contains several node ordering techniques, such as trivial, RN and community-based ordering.…”

Section: Software Functionalitiesmentioning

confidence: 99%

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Técnicas avançadas para análise visual de redes temporais

Linhares¹

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To my parents, Enivaldo and Elcione, and my brother, Hélder, by the love, incentive and unconditional support, because without them, nothing would be possible. To my beautiful lifemate, Sara Fernandes, that always give me strength and support in all my decisions. Her dedication inspires me and it is very important to the conclusion of this thesis. To my friends Jean Ponciano and Everton Lira that were a crucial part of all my academic courses, always being helpful and partners. To the professors Bruno Travençolo, José Gustavo and Luis Rocha, by the guidance, support, and trust. To all that were directly or indirectly a part of my formation, I appreciate it. To the research funding agency CAPES for the scholarship granted to me.

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“…Visual clutter refers to excessive items or information close together due to edge and node overlap (ELLIS;DIX, 2007). To reduce clutter in such layouts, some efforts focus on summarizing the network by changing its original temporal resolution (i.e., by grouping edges from subsequent timestamps - Figure 1(a)) (LIN-HARES et al, 2017b;ZHAO et al, 2018;LINHARES et al, 2019a;HOLME, 2017) or by selecting relevant edges for analysis (Figure 1(b)) (ZHAO et al, 2018;HOLME, 2017;ZHAO et al, 2019). Other strategies, on the other hand, focus on changing node positioning instead of summarizing the network.…”

Section: Introductionmentioning

confidence: 99%

Improving visual analysis of streaming networks

Ponciano¹

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Temporal networks (also known as dynamic networks) are often used to model connections that occur over time between parts of a system by using nodes and edges. In temporal networks, all nodes, edges, and times, are known and available to be used in the analysis. However, in several real-world applications, data are produced in a massive and continuous way, which is known as data stream. In this case, the volume of data may be so large that the storage may be impossible and mining tasks become more challenging. In streaming temporal networks, edges are continuously arriving in non-stationary distribution. In both temporal and streaming temporal networks, patterns related to node and edge activity are typically irregular in time, which makes the visualization of such networks helpful to gain insights about network structure and dynamics. Nevertheless, the non-stationary distribution of incoming data increases complexity and turns the streaming temporal network visualization even more challenging. Several visualization layouts have been proposed, but they all have limitations. The main challenge in this context is the amount of visual information, that increases depending on the network size and density, and causes visual clutter due to edge overlap, fine temporal resolution, and node proximity. In this thesis, we propose methods to enhance the visualization of streaming temporal networks through the manipulation of the three network dimensions, namely node, edge, and time. Specifically, we propose: (i) CNO, a visual scalable node ordering method; (ii) SEVis, a streaming edge sampling method; and (iii) a streaming method that adapts the temporal resolution according to local levels of node activity. We also present a comparative study considering the combination of these methods. We show through case studies with real-world networks that each of these methods greatly improves layout readability, thus leading to a fast and reliable decision making.

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EOD Edge Sampling for Visualizing Dynamic Network via Massive Sequence View

Cited by 27 publications

References 36 publications

Visual Analysis of Contact Patterns in School Environments

Visual Analysis of Contact Patterns in School Environments

Técnicas avançadas para análise visual de redes temporais

Improving visual analysis of streaming networks

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