Visual Anomaly Detection in Temporal Knowledge Graphs
Magdalena Allmann - RPTU in Kaiserslautern, Kaiserslautern, Germany
Kevin Iselborn - RPTU in Kaiserslautern, Kaiserslautern, Germany
Jan-Tobias Sohns - University of Kaiserslautern-Landau, Kaiserslautern, Germany
Heike Leitte - University of Kaiserslautern-Landau, Kaiserslautern, Germany
Room: Bayshore II
2024-10-13T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-13T12:30:00Z
Full Video
Abstract
This paper addresses the visualization challenges posed by Mini Challenge 3 of the VAST Challenge 2024, which involves detecting illegal fishing activities within a dynamic network of companies and individuals. The task requires effective anomaly detection in a time-dependent knowledge graph, a scenario where conventional graph visualization tools often fall short due to their limited ability to integrate temporal data and the undefined nature of the anomalies. We demonstrate how to overcome these challenges through well-crafted views implemented in standard software libraries. Our approach involves decomposing the time-dependent knowledge graph into separate time and structure components, as well as providing data-driven guidance for identifying anomalies. These components are then interconnected through extensive interactivity, enabling exploration of anomalies in a complex, temporally evolving network. The source code and a demonstration video are publicly available at github.com/MaAllma/Temporal/Knowledge/Graph/Analysis.