Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization

By K. Wongsuphasawat, David Gotz. 2012

236 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Towards Better Analysis of Deep Convolutional Neural Networks (2016, background): “A large CNN may contain millions of connections between neurons.”
Visual Analysis of Topic Competition on Social Media (2013, methodology): “Outflow [46] applied sankey diagram to visualize temporal event sequence.”
EventAction (2019, methodology): “For example, OutFlow [82] uses a network structure to aggregate similar event sequences into progression pathways and summarizes the pathways’ possible outcomes.”
StoryFlow: Tracking the Evolution of Stories (2013, methodology): “LifeFlow [46] and Outflow [45] merged multiple event sequences into a tree and a graph, respectively, to provide an abstraction of the data and enable more scalable timeline visualizations.”
How Hierarchical Topics Evolve in Large Text Corpora (2014, background): “LifeFlow [39] and Outflow [38] facilitate the exploration of temporal event sequences.”
Sequence Synopsis: Optimize Visual Summary of Temporal Event Data (2018, background): “Methods such as Sankey diagrams [54] can not effectively handle noisy data with high event cardinality.”