Research Projects

The field of graph drawing is crucial in theoretical computer science, offering essential tools for visualizing complex networks across various domains, including biology, social sciences, and computer networks. However, traditional graph drawing techniques often fall short when addressing the dynamic and parameterized nature of real-world data. This proposal aims to overcome these limitations by integrating parameterized complexity and temporal graph dynamics into graph drawing methodologies. Parameterized graph drawing leverages specific graph parameters to develop efficient algorithms capable of handling large and complex networks. In contrast, temporal graph drawing addresses the evolving nature of many real-world networks, enabling the visualization of dynamic network evolution and providing deeper insights into temporal patterns and trends. Despite the significance of research in these areas, many fundamental problems remain unexplored. Research in parameterized graph drawing has primarily focused on a few topics, with limited use of advanced parameterized tools and techniques. Our proposed research aims to address these gaps by developing new tools and frameworks and introducing drawing-specific parameters. These advancements will enable the use of both standard and advanced parameterized techniques more efficiently for graph drawing problems. Meanwhile, research in temporal graph drawing is almost non-existent. Our proposed research aims to address and fill these gaps. Our proposed research aims to fill this gap by developing new techniques and algorithms that incorporate and deal with temporal information, enabling the visualization of changes in graph structure over time. This will allow users to gain insights into the evolution of networks, identify patterns, and make informed decisions based on temporal trends.