Maximum fair cliques have far-reaching implications across different domains. In collaboration networks, assembling a balanced team with diverse skills and perspectives can boost creativity and problem-solving abilities. Social networks can benefit from larger and more connected teams to promote product exposure and influence. Film production can leverage maximum fair cliques to create successful movies by combining established actors with fresh talent.
Techniques
Several techniques have been proposed to solve the maximum fair clique problem, including EnColorfulCore, ColorfulSup, and EnColorfulSup. These methods aim to balance the number of males and females while ensuring the team size meets the threshold value (k). Additionally, graph reduction techniques like adversarial learning can help mitigate unwanted biases in deep image representations.
Results
Real-world datasets were analyzed to demonstrate the effectiveness of maximum fair cliques. In the Aminer dataset, which contains information on authors’ genders, a balance between males and females was maintained while ensuring the team size was not less than k=5. The results showed that invoking the proposed algorithms resulted in a team with 13 males (colored blue) and 16 females (colored red), maintaining a fair balance without exceeding the difference limit of δ=3.
Conclusion
Maximum fair cliques are a critical concept in graph analysis, offering numerous applications across diverse domains. By balancing the number of males and females within a team while ensuring the team size meets a minimum threshold value, maximum fair cliques can enhance collaboration, product exposure, and creativity. The proposed techniques, such as EnColorfulCore, ColorfulSup, and adversarial learning, have demonstrated effectiveness in solving the maximum fair clique problem. By leveraging these techniques, organizations can make informed decisions that promote diversity and inclusivity while maintaining a balance between competing factors.
