IEEE VIS 2024 Content: AdaMotif: Graph Simplification via Adaptive Motif Design

AdaMotif: Graph Simplification via Adaptive Motif Design

Hong Zhou - Shenzhen University, Shenzhen, China

Peifeng Lai - Shenzhen University, Shenzhen, China

Zhida Sun - Shenzhen University, Shenzhen, China

Xiangyuan Chen - Shenzhen University, Shenzhen, China

Yang Chen - Shenzhen University, Shen Zhen, China

Huisi Wu - Shenzhen University, Shenzhen, China

Yong WANG - Nanyang Technological University, Singapore, Singapore

Room: Bayshore VII

2024-10-18T13:18:00ZGMT-0600Change your timezone on the schedule page
2024-10-18T13:18:00Z
Exemplar figure, described by caption below
Case analysis of the Cpan dataset: (a) the original graph; (b) our AdaMotif. The highlighted areas of each subfigure show the enlarged communities. We highlight identical communities for comparison. The identical communities are marked using "The same community". In (a), to make communities easier to identify, their nodes and edges are highlighted in blue and red, respectively. In (b), motifs with the same color and similar shape represent similar communities. The size of the motif indicates the number of nodes in this community. Our result provides a clearer expression of community information.
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Keywords

Graph visualization, node-link diagrams, graph simplification

Abstract

With the increase of graph size, it becomes difficult or even impossible to visualize graph structures clearly within the limited screen space. Consequently, it is crucial to design effective visual representations for large graphs. In this paper, we propose AdaMotif, a novel approach that can capture the essential structure patterns of large graphs and effectively reveal the overall structures via adaptive motif designs. Specifically, our approach involves partitioning a given large graph into multiple subgraphs, then clustering similar subgraphs and extracting similar structural information within each cluster. Subsequently, adaptive motifs representing each cluster are generated and utilized to replace the corresponding subgraphs, leading to a simplified visualization. Our approach aims to preserve as much information as possible from the subgraphs while simplifying the graph efficiently. Notably, our approach successfully visualizes crucial community information within a large graph. We conduct case studies and a user study using real-world graphs to validate the effectiveness of our proposed approach. The results demonstrate the capability of our approach in simplifying graphs while retaining important structural and community information.