Efficient Algorithms for Visualizing Signed Networks

Network visualization is a powerful tool for understanding complex relational data such as social networks, software dependencies, and transportation systems. Traditional visualization methods reveal communities or dense subnetworks, but often miss important details when networks include positive and negative influences or direction of flow, such as who influences whom on Twitter or how bugs spread in software. To overcome this limitation, our research develops innovative visualization techniques that make directional relationships more intuitive and interpretable. These methods aim to generate high-quality visual summaries that preserve critical structural properties such as directionality, information flow, and community organization, thereby offering deeper insights into the dynamics of real-world networks.

Faculty Supervisor:

Debajyoti Mondal

Student:

Partner:

SRM Institute of Science and Technology

Discipline:

Computer science

Sector:

Information and Communications Technology

University:

University of Saskatchewan

Program: