Boundary Node Detection and Unfolding of Complex Non-Convex Ad Hoc Networks

TL;DR

This paper introduces a novel algorithm for detecting boundary nodes and unfolding stacked regions in complex non-convex ad hoc networks, enhancing visualization and analysis capabilities.

Contribution

It proposes the W-KK-MS algorithm for boundary detection and a new method for unfolding stacked regions, with a prototype system ELnet for network analysis.

Findings

Fast convergence in boundary node detection

Successful unfolding of stacked regions

Effective visualization of complex networks

Abstract

Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and edges. In many instances, the layouts of these networks are not entirely convex in shape. In this article, we propose a Kamada-Kawai-based algorithm called W-KK-MS for boundary node detection problems, which is capable of aligning node positions while achieving high sensitivity, specificity, and accuracy in producing a visual drawing from the input network topology. The algorithm put forward in this article selects and assigns weights to top-k nodes in each iteration to speed up the updating process of nodes. We also propose a novel approach to detect and unfold stacked regions in CNCAH networks. Experimental results show that the proposed algorithms can achieve fast convergence on boundary node detection in CNCAH networks and are able to successfully unfold stacked regions. The design and implementation of a prototype system called ELnet for analyzing CNCAH networks is also described in this article. The ELnet system is capable of generating synthetic networks for testing, integrating with force-directed algorithms, and visualizing and analyzing algorithms' outcomes.