Attribute-Balanced Fair Influence Maximization via Strategic Group Competition

Fair influence maximization aims to select influential seed nodes while ensuring that information spreads fairly across different groups. Existing methods often define fairness over a single group partition or treat all groups as a flat collection. This becomes limiting when several categorical attributes are considered at the same time, because attributes with more groups may have greater influence on the selection process, and raw group utilities may not distinguish between persistent under-service and limited structural opportunity. We propose SAGE-IM, a multi-attribute fair influence maximization framework in which each attribute-group pair is modeled as a strategic agent. SAGE-IM combines opportunity-normalized deficits, cross-step regret, and hierarchical attribute balancing to adapt fairness pressure as the seed set grows. Group-specific teachers learn RR-based marginal utilities, and their preferences are distilled into a shared student through policy, ranking, and representation objectives. During sequential selection, the learned models are used to generate a compact candidate set, while the final seed choice is based on current global and group-specific reverse-reachable marginals. This design reduces reliance on learned rankings and keeps the final decision tied to the sampled diffusion structure. Experiments on eight processed real-world networks show that SAGE-IM achieves strong attribute-wise fairness and worst-group coverage while maintaining competitive influence spread. Sensitivity and ablation analyses further show how fairness dynamics, strategic coordination, attribute balancing, and active-agent selection shape the spread-fairness trade-off.