  This research study investigates the minimization of inequality in the ranks
of vertices obtained using the PageRank algorithm. PageRank is a widely used
algorithm for ranking webpages and plays a significant role in determining web
traffic. This study employs the Gini coefficient, a measure of income/wealth
inequality, to assess the inequality in PageRank distributions on various types
of graphs. The investigation involves two experiments: one that modifies
strategies for handling dead-end nodes and another that explores six
deterministic methods for reducing inequality. Our findings indicate that a
combination of two distinct heuristics may present an effective strategy for
minimizing inequality.
