Curve Segment Neighborhood-based Vector Field Exploration

TL;DR

This paper introduces a novel graph-based method for analyzing vector fields using curve segments, enabling multi-level interactive exploration of flow features in large datasets.

Contribution

It proposes the Curve Segment Neighborhood Graph (CSNG) and adapts community detection and force-directed layout techniques for interactive vector field analysis.

Findings

Communities in CSNG often correspond to flow features

The system supports multi-level, interactive exploration

Effective analysis of large-scale integral curve datasets

Abstract

Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louvain algorithm, to identify individual graph communities from CSNG. Our results show that these communities often correspond to the features of the flow. To achieve a multi-level interactive exploration of the detected communities, we adapt a force-directed layout that allows users to refine and re-group communities based on their domain knowledge. We incorporate the proposed techniques into an interactive system to enable effective analysis and interpretation of complex patterns in large-scale integral curve datasets.