HoNVis: Visualizing and exploring higher-order networks

Tao, Jun; Xu, Jian; Wang, Chaoli; Chawla, Nitesh V.

doi:10.1109/pacificvis.2017.8031572

Cited by 4 publications

(2 citation statements)

References 25 publications

(34 reference statements)

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“…It allows teachers to easily study student behavior patterns and identify the course resources that are most or least often clicked. The tool uses a higher-order network algorithm [44] to highlight critical sequences leading to different transition probabilities in order to study large-scale functions in the node-link diagram. CCVis correlates the click behavior pattern with the distribution of grades on the chart; thus, users can see what grades they will receive given a specific behavioral model.…”

Section: Discussionmentioning

confidence: 99%

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Zotov

Ibrahim

Petunina

et al. 2021

Int. J. Emerg. Technol. Learn.

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The study describes an approach to e-learning based on the Moodle platform that is used to visualize participation in the learning community and is proposed to be used to inform students and teachers about their involvement in the social learn-ing environment. The experiment involved 5 teachers and 3 experts who deter-mined the most significant visualization indicators for the virtual learning envi-ronment dashboards. There were 42 students aged 21 to 23. The virtual learning environment is based on the Moodle and Blackboard platforms that are common-ly used in universities. SocialWall allowed participants to perform actions in the social environment that are visualized in graphs under the specified criteria. A Wiki repository plugin was also added in order to accumulate student knowledge in shared structured documents stored in a shared repository. The relational data-base management system MySQL allows creation of additional relations, data-base design and administration. The visualization activities described in the study are based on modified state transition networks to analyze and visualize the stu-dent learning path. Student trajectory networks show the interaction of individual learners or groups with the course structure and material.

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Section: Discussionmentioning

confidence: 99%

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Zotov

Ibrahim

Petunina

et al. 2021

Int. J. Emerg. Technol. Learn.

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“…Processing a rich set of information and complex dependencies in higher-order networks are major obstacles to pattern discovery and interpretation. HoN-Vis [14] delivered a significant contribution in this area. With a global shipping network as an example, it demonstrated how an interactive exploration of higher-order networks could help a decision process.…”

Section: Higher-order Network Visualizationmentioning

confidence: 99%

CPViz: Visualizing clinical pathways represented in higher-order networks

Chae¹,

Park²,

Kim³

et al. 2023

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To improve clinical care practice, it is important to understand the variability of clinical pathways executed in different contexts (e.g., pathways in different geographical locations, demographics, and phenotypic groups). A common way of representing clinical pathways is through network-based representations that capture the trajectories of treatment steps. However, first-order networks, which are based on the Markovian property and the de facto standard model to represent transitions between steps, often fail to capture real trajectories. This paper introduces a visual analytic tool to explore and compare pathways represented in higher-order networks. Because each higher node in the network is a sub-trajectory (i.e., partial or full history of treatment steps), the tool can display true sequences of treatment steps and compute the similarity of the two networks in the space of higher-order nodes. The tool also highlights areas where the two networks are similar and dissimilar and how a certain subtrajectory is realized differently in different pathways. The paper demonstrates the tool's usefulness by applying it to multiple antidepressant pharmacotherapy pathways for veterans diagnosed with major depressive disorder and by illustrating heterogeneity in prescription patterns across pathways.

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Event detection in temporal social networks using a higher-order network model

Zhang

Huangpeng

et al. 2021

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Event detection is one of the most important areas of complex network research. It aims to identify abnormal points in time corresponding to social events. Traditional methods of event detection, based on first-order network models, are poor at describing the multivariate sequential interactions of components in complex systems and at accurately identifying anomalies in temporal social networks. In this article, we propose two valid approaches, based on a higher-order network model, namely, the recovery higher-order network algorithm and the innovation higher-order network algorithm, to help with event detection in temporal social networks. Given binary sequential data, we take advantage of chronological order to recover the multivariate sequential data first. Meanwhile, we develop new multivariate sequential data using logical sequence. Through the efficient modeling of multivariate sequential data using a higher-order network model, some common multivariate interaction patterns are obtained, which are used to determine the anomaly degree of a social event. Experiments in temporal social networks demonstrate the significant performance of our methods finally. We believe that our methods could provide a new perspective on the interplay between event detection and the application of higher-order network models to temporal networks.

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HoNVis: Visualizing and exploring higher-order networks

Cited by 4 publications

References 25 publications

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

Engagement of Students in Data Visualization for the Purpose of E-Learning Improvement

CPViz: Visualizing clinical pathways represented in higher-order networks

Event detection in temporal social networks using a higher-order network model

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