Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations

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Moataz Abdelaal, Nathan D Schiele, Katrin Angerbauer, Kuno Kurzhals, Michael Sedlmair, Daniel Weiskopf

 View presentation:2022-10-20T16:21:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T16:21:00Z

Exemplar figure, described by caption below — Three visualization techniques for the representation of large networks. Node-link diagrams and adjacency matrices are common in visualization. Bipartite layouts have been proposed as an alternative for solving different tasks. We compare all three techniques with respect to different network properties and tasks.

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Abstract

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.