IEEE VIS 2024 Content: MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks with Temporal Network Motifs

MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks with Temporal Network Motifs

Seokweon Jung -

DongHwa Shin -

Hyeon Jeon -

Kiroong Choe -

Jinwook Seo -

DOI: 10.1109/TVCG.2023.3337396

Room: Bayshore I

2024-10-16T18:33:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T18:33:00Z
Exemplar figure, described by caption below
MoNetExplorer is a visual analytics system designed to support the selection of appropriate window sizes for dynamic network analysis and provides a temporal and structural analysis of snapshots that are sliced according to window sizes. The system is composed of five linked components. (A) Slicing Navigation View supports the beginning of the workflow: selection of snapshot window sizes according to measures based on Temporal Network Motifs (TNM). (B) Temporal Measure View and (C) Temporal Status View enable validation of the quality of snapshots and identification of temporal patterns. (D) Motif Composition View visualizes the composition of temporal network motifs. (E) Bottom-level details of network structure are shown in Network View.
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Keywords

Visual analytics, Measurement, Size measurement, Windows, Time measurement, Data visualization, Task analysis, Visual analytics, Dynamic networks, Temporal network motifs, Interactive network slicing

Abstract

Partitioning a dynamic network into subsets (i.e., snapshots) based on disjoint time intervals is a widely used technique for understanding how structural patterns of the network evolve. However, selecting an appropriate time window (i.e., slicing a dynamic network into snapshots) is challenging and time-consuming, often involving a trial-and-error approach to investigating underlying structural patterns. To address this challenge, we present MoNetExplorer, a novel interactive visual analytics system that leverages temporal network motifs to provide recommendations for window sizes and support users in visually comparing different slicing results. MoNetExplorer provides a comprehensive analysis based on window size, including (1) a temporal overview to identify the structural information, (2) temporal network motif composition, and (3) node-link-diagram-based details to enable users to identify and understand structural patterns at various temporal resolutions. To demonstrate the effectiveness of our system, we conducted a case study with network researchers using two real-world dynamic network datasets. Our case studies show that the system effectively supports users to gain valuable insights into the temporal and structural aspects of dynamic networks.