Scalable Algorithms for Information Centrality Optimization via Global Edge Addition
This work proposes two scalable greedy algorithms based on gradient-guided edge selection that efficiently approximate marginal gains and prune the candidate edge set and achieves optimization performance comparable to exact greedy strategies while substantially reducing computational time.