Efficient Maximum Fair Clique Search over Large Networks

TL;DR

This paper introduces novel algorithms and techniques for efficiently finding maximum relative fair cliques in large attributed graphs, addressing fairness and scalability in cohesive subgraph mining.

Contribution

It pioneers the investigation of maximum relative fair clique detection, proposing graph reduction, upper bounds, and a linear-time heuristic algorithm for large-scale graphs.

Findings

Algorithms are efficient and scalable on real datasets.

Graph reduction techniques effectively prune the search space.

Heuristic algorithm improves search efficiency and solution quality.

Abstract

Mining cohesive subgraphs in attributed graphs is an essential problem in the domain of graph data analysis. The integration of fairness considerations significantly fuels interest in models and algorithms for mining fairness-aware cohesive subgraphs. Notably, the relative fair clique emerges as a robust model, ensuring not only comprehensive attribute coverage but also greater flexibility in distributing attribute vertices. Motivated by the strength of this model, we for the first time pioneer an investigation into the identification of the maximum relative fair clique in large-scale graphs. We introduce a novel concept of colorful support, which serves as the foundation for two innovative graph reduction techniques. These techniques effectively narrow the graph's size by iteratively removing edges that do not belong to relative fair cliques. Furthermore, a series of upper bounds of the maximum relative fair clique size is proposed by incorporating consideration of vertex attributes and colors. The pruning techniques derived from these upper bounds can significantly trim unnecessary search space during the branch-and-bound procedure. Adding to this, we present a heuristic algorithm with a linear time complexity, employing both a degree-based greedy strategy and a colored degree-based greedy strategy to identify a larger relative fair clique. This heuristic algorithm can serve a dual purpose by aiding in branch pruning, thereby enhancing overall search efficiency. Extensive experiments conducted on six real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms.