Clustering is a fundamental task in machine learning and data analysis, but it frequently fails to provide fair representation for various marginalized communities defined by multiple protected attributes -- a shortcoming often caused by biases in the training data. As a result, there is a growing need to enhance the fairness of clustering outcomes, ideally by making minimal modifications, possibly as a post-processing step after conventional clustering. A recent work initiated the study of closest fair clustering, though in a restricted scenario where data points belong to only two groups. In practice, however, data points are typically characterized by many groups, reflecting diverse protected attributes such as age, ethnicity, gender, etc. In this work, we generalize the study of the closest fair clustering problem to settings with an arbitrary number (more than two) of groups. We begin by showing that the problem is NP-hard even when all groups are of equal size -- a stark contrast with the two-group case, for which an exact algorithm exists. Next, we propose near-linear time approximation algorithms that efficiently handle arbitrary-sized multiple groups. Leveraging our closest fair clustering algorithms, we further achieve improved approximation guarantees for the fair correlation clustering problem, advancing the state-of-the-art results. Additionally, we are the first to provide approximation algorithms for the fair consensus clustering problem involving multiple (more than two) groups.

Consensus clustering, a fundamental task in machine learning and data analysis, aims to aggregate multiple input clusterings of a dataset, potentially based on different non-sensitive attributes, into a single clustering that best represents the collective structure of the data. In this work, we study this fundamental problem through the lens of fair clustering, as introduced by Chierichetti et al. [NeurIPS’17], which incorporates the disparate impact doctrine to ensure proportional representation of each protected group in the dataset within every cluster. Our objective is to find a consensus clustering that is not only representative but also fair with respect to specific protected attributes. To the best of our knowledge, we are the first to address this problem and provide a constant-factor approximation. As part of our investigation, we examine how to minimally modify an existing clustering to enforce fairness – an essential postprocessing step in many clustering applications that require fair representation. We develop an optimal algorithm for datasets with equal group representation and near-linear time constant factor approximation algorithms for more general scenarios with different proportions of two group sizes. We complement our approximation result by showing that the problem is NP-hard for two unequal-sized groups. Given the fundamental nature of this problem, we believe our results on Closest Fair Clustering could have broader implications for other clustering problems, particularly those for which no prior approximation guarantees exist for their fair variants.

In this paper, we consider reachability oracles and reachability preservers for directed graphs/networks prone to edge/node failures. Let G = (V, E) be a directed graph on n-nodes, and P ⊆ V× V be a set of vertex pairs in G. We present the first non-trivial constructions of single and dual fault-tolerant pairwise reachability oracle with constant query time. Furthermore, we provide extremal bounds for sparse fault-tolerant reachability preservers, resilient to two or more failures. Prior to this work, such oracles and reachability preservers were widely studied for the special scenario of single-source and all-pairs settings. However, for the scenario of arbitrary pairs, no prior (non-trivial) results were known for dual (or more) failures, except those implied from the single-source setting. One of the main questions is whether it is possible to beat the O(n |P|) size bound (derived from the single-source setting) for reachability oracle and preserver for dual failures (or O(2^k n|P|) bound for k failures). We answer this question affirmatively. Below we summarize our contributions. - For an n-vertex directed graph G = (V, E) and P ⊆ V× V, we present a construction of O(n √{|P|}) sized dual fault-tolerant pairwise reachability oracle with constant query time. We further provide a matching (up to the word size) lower bound of Ω(n √{|P|}) on the size (in bits) of the oracle for the dual fault setting, thereby proving that our oracle is (near-)optimal. - Next, we provide a construction of O(n + min{|P|√ n,~n√{|P|}}) sized oracle with O(1) query time, resilient to single node/edge failure. In particular, for |P| bounded by O(√n) this yields an oracle of just O(n) size. We complement the upper bound with a lower bound of Ω(n^{2/3}|P|^{1/2}) (in bits), refuting the possibility of a linear-sized oracle for P of size ω(n^{2/3}). - We also present a construction of O(n^{4/3} |P|^{1/3}) sized pairwise reachability preservers resilient to dual edge/vertex failures. Previously, such preservers were known to exist only under single failure and had O(n+min{|P|√n,~n√ {|P|}}) size [Chakraborty and Choudhary, ICALP'20]. We also show a lower bound of Ω(n √{|P|}) edges on the size of dual fault-tolerant reachability preservers, thereby providing a sharp gap between single and dual fault-tolerant reachability preservers for |P| = o(n). - Finally, we provide a generic pairwise reachability preserver construction that provides a o(2^k n |P|) sized subgraph resilient to k failures, for any k ≥ 1. Before this work, we only knew of an O(2^k n |P|) bound implied from the single-source setting [Baswana, Choudhary, and Roditty, STOC'16].