Publikation: Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models
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2019
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European Union (EU): 700381
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ASGARD - Analysis System For Gathered Raw Data
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Proceeedings of the Set Visual Analytics Workshop at IEEE VIS 2019. 2019
Zusammenfassung
Members of communities often share topics of interest. However, usually not all members are interested in all topics, and participation in topics changes over time. Prediction models based on temporal hypergraphs that—in contrast to state-of-the-art models—exploit group structures in the communication network can be used to anticipate changes of interests. In practice, there is a need to assess these models in detail. While loss functions used in the training process can provide initial cues on the model’s global quality, local quality can be investigated with visual analytics. In this paper, we present a visual analytics framework for the assessment of temporal hypergraph prediction models. We introduce its core components: a sliding window approach to prediction and an interactive visualization for partially fuzzy temporal hypergraphs.
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004 Informatik
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Set Visual Analytics Workshop at IEEE VIS 2019, 20. Okt. 2019, Vancouver, Canada
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STREEB, Dirk, Devanshu ARYA, Daniel A. KEIM, Marcel WORRING, 2019. Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models. Set Visual Analytics Workshop at IEEE VIS 2019. Vancouver, Canada, 20. Okt. 2019. In: Proceeedings of the Set Visual Analytics Workshop at IEEE VIS 2019. 2019BibTex
@inproceedings{Streeb2019Visua-47306,
year={2019},
title={Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models},
url={https://scibib.dbvis.de/publications/view/838},
booktitle={Proceeedings of the Set Visual Analytics Workshop at IEEE VIS 2019},
author={Streeb, Dirk and Arya, Devanshu and Keim, Daniel A. and Worring, Marcel}
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<dcterms:abstract xml:lang="eng">Members of communities often share topics of interest. However, usually not all members are interested in all topics, and participation in topics changes over time. Prediction models based on temporal hypergraphs that—in contrast to state-of-the-art models—exploit group structures in the communication network can be used to anticipate changes of interests. In practice, there is a need to assess these models in detail. While loss functions used in the training process can provide initial cues on the model’s global quality, local quality can be investigated with visual analytics. In this paper, we present a visual analytics framework for the assessment of temporal hypergraph prediction models. We introduce its core components: a sliding window approach to prediction and an interactive visualization for partially fuzzy temporal hypergraphs.</dcterms:abstract>
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2019-10-24
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