Creating Robust and Fair Graph Structures for Connectivity and Clustering

TL;DR

This thesis advances graph algorithms by developing fault-tolerant reachability preservers and fair clustering methods, enhancing robustness and fairness in large-scale systems.

Contribution

It introduces the first dual fault-tolerant reachability preservers and novel fair clustering algorithms with improved guarantees and streaming capabilities.

Findings

Sparse dual fault-tolerant reachability preservers of size O(n^{4/3}| ext{P}|^{1/3})

Approximation algorithms for fair consensus clustering with better guarantees

First streaming algorithm for fair consensus clustering with logarithmic memory

Abstract

Graph algorithms are central to large-scale applications such as navigation systems, social networks, and data analysis platforms. This thesis studies two important challenges in such systems: robustness to failures and fairness in clustering outcomes. In the first part, we investigate fault-tolerant reachability preservers in directed graphs. We present the first non-trivial constructions of dual fault-tolerant pairwise reachability preservers that remain resilient to two edge or vertex failures, achieving a sparse construction of size $O(n^{4/3}|\mathcal{P}|^{1/3})$. In the second part, we study fair clustering algorithms that ensure balanced representation of protected groups. We develop approximation algorithms for fair consensus clustering and introduce the framework of closest fair clustering, establishing hardness results and efficient algorithms for multi-group settings. Building on this framework, we obtain improved guarantees for fair correlation clustering and design the first streaming algorithm for fair consensus clustering using only logarithmic memory. Together, these results contribute toward the design of graph algorithms that are both robust and socially responsible.