Fairness-aware Maximal Clique in Large Graphs: Concepts and Algorithms

TL;DR

This paper introduces fairness-aware maximal clique models for attributed graphs, along with efficient algorithms for enumerating these cliques, addressing a gap in cohesive subgraph mining with fairness considerations.

Contribution

It proposes three novel fairness-aware maximal clique models and develops efficient enumeration algorithms with pruning and ordering techniques.

Findings

Algorithms are efficient and scalable on real-world graphs.

Proposed methods effectively incorporate fairness into clique mining.

Experimental results demonstrate the effectiveness of the algorithms.

Abstract

Cohesive subgraph mining on attributed graphs is a fundamental problem in graph data analysis. Existing cohesive subgraph mining algorithms on attributed graphs do not consider the fairness of attributes in the subgraph. In this paper, we, for the first time, introduce fairness into the widely-used clique model to mine fairness-aware cohesive subgraphs. In particular, we propose three novel fairness-aware maximal clique models on attributed graphs, called weak fair clique, strong fair clique and relative fair clique, respectively. To enumerate all weak fair cliques, we develop an efficient backtracking algorithm called WFCEnum equipped with a novel colorful k-core based pruning technique. We also propose an efficient enumeration algorithm called SFCEnum to find all strong fair cliques based on a new attribute-alternatively-selection search technique. To further improve the efficiency, we also present several non-trivial ordering techniques for both weak and strong fair clique enumerations. To enumerate all relative fair cliques, we design an enhanced colorful k-core based pruning technique for 2D attribute, and then develop two efficient search algorithms: RFCRefineEnum and RFCAlterEnum based on the ideas of WFCEnum and SFCEnum for arbitrary dimension attribute. The results of extensive experiments on four real-world graphs demonstrate the efficiency, scalability and effectiveness of the proposed algorithms.