Detecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi‐Source Event Sequences

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dc.contributor.authorXu, Shaobinen_US
dc.contributor.authorSun, Minghuien_US
dc.date.accessioned2025-03-07T16:48:36Z
dc.date.available2025-03-07T16:48:36Z
dc.date.issued2024
dc.description.abstractUncovering causal relations from event sequences to guide decision‐making has become an essential task across various domains. Unfortunately, this task remains a challenge because real‐world event sequences are usually collected from multiple sources. Most existing works are specifically designed for homogeneous causal analysis between events from a single source, without considering cross‐source causality. In this work, we propose a heterogeneous causal analysis algorithm to detect the heterogeneous causal network between high‐level events in multi‐source event sequences while preserving the causal semantic relationships between diverse data sources. Additionally, the flexibility of our algorithm allows to incorporate high‐level event similarity into learning model and provides a fuzzy modification mechanism. Based on the algorithm, we further propose a visual analytics framework that supports interpreting the causal network at three granularities and offers a multi‐granularity modification mechanism to incorporate user feedback efficiently. We evaluate the accuracy of our algorithm through an experimental study, illustrate the usefulness of our system through a case study, and demonstrate the efficiency of our modification mechanisms through a user study.en_US
dc.description.number1
dc.description.sectionheadersOriginal Article
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.15267
dc.identifier.issn1467-8659
dc.identifier.pages17
dc.identifier.urihttps://doi.org/10.1111/cgf.15267
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15267
dc.publisherEurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd.en_US
dc.subjectcausal analysis
dc.subjectheterogeneous causal network
dc.subjectmulti‐source event sequence
dc.subjectvisual analytics
dc.subject• Human‐centered computing → Visualization systems and tools; Visual analytics
dc.titleDetecting, Interpreting and Modifying the Heterogeneous Causal Network in Multi‐Source Event Sequencesen_US
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