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Nguyen K. Phan

dblp:351/8513 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0009-9253-7115ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › scientific visualization › field visualization
vector field visualization
0.812024
Curve Segment Neighborhood-based Vector Field Exploration · IEEE VIS 2024

Methods — techniques the papers use, named apart from their topics

louvain algorithm · 0.8force-directed layout · 0.8
YearPublicationVenuePosition
2026 Interactive exploration of large-scale streamlines of vector fields via a Curve Segment Neighborhood Graph
Nguyen K. Phan, Adeel Zafar, Guoning Chen
Comput. Graph.1
2024 Curve Segment Neighborhood-based Vector Field Exploration
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.
Nguyen K. Phan, Guoning Chen
IEEE VIS1