Overview
This project pioneers the integration of fairness and diversity into graph algorithm design, addressing critical theoretical and computational challenges. Through innovative approaches to, e.g., diverse community detection, subgraph discovery, and scalable graph decomposition, the research directly contributes to the development of ethical, inclusive, and socially responsible graph algorithms with real-world impact.
About this opportunity
Graphs and networks are everywhere, powering critical systems that billions of people rely on daily—social networks for communication, infrastructure networks for energy and transportation, and communication networks for global connectivity. These interconnected systems profoundly shape society, making them a critical area of research. However, existing approaches to analysing and designing algorithms for these networks often overlook fairness and diversity, leading to inequities such as biased resource allocation or exclusion of underrepresented groups.
In recent years, there has been increasing interest in incorporating fairness and diversity into algorithmic graph frameworks, particularly in areas like equitable community detection and fairness in resource allocation. However, existing approaches often focus on limited fairness or diversity metrics, lack scalability for large real-world networks, or fail to explore the trade-offs between different metric variants. Moreover, the wide variety of fairness and diversity metrics presents a significant challenge in selecting the most appropriate ones for specific applications, especially when these metrics can conflict with one another.
This project addresses these gaps by systematically investigating the theoretical, computational, and practical challenges of integrating fairness and diversity into graph algorithms. The research will advance scalable methods for tasks such as subgraph discovery, graph decomposition, and beyond. These methods will be applied to real-world contexts, such as ensuring equitable access to energy and transportation or promoting balanced community representation in social networks. By addressing these challenges, the project will contribute to the growing need for ethical and socially responsible algorithmic systems.
Initially based at NTHU for 18 months, the candidate will build foundational expertise in graph theory, combinatorial optimization, and advanced data structures, formulating mathematical models and algorithms specifically targeting fairness and diversity challenges. Subsequently, during 24 months at the University of Liverpool (UoL), the candidate will engage deeply with advanced algorithmic design and performance analysis, creating efficient, scalable solutions and evaluating their effectiveness on real-world datasets. The final 6 months back at NTHU will focus on refining research findings and preparing the doctoral thesis.
The candidate will benefit from exceptional resources and expertise at both NTHU and UoL, engaging in vibrant research communities known for graph mining, machine learning, scalable algorithms, and social network analysis. This project offers a unique opportunity for ambitious researchers interested in addressing societal inequities through advanced algorithmic research, promoting ethical, inclusive, and equitable networked systems.
Who is this opportunity for?
This project is open to UK and international applicants. Candidates will have, or be due to obtain, a master’s degree or equivalent from a reputable University in an appropriate field of Science and Engineering.
We want all of our staff and students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. We believe everyone deserves an excellent education and encourage students from all backgrounds and personal circumstances to apply.