Fair Influence Maximization beyond Predefined Groups

Fair influence maximization aims to spread information widely while avoiding large disparities across different parts of a network. Most existing methods assume that protected groups or community labels are known. This assumption is often unrealistic when demographic information is missing, incomplete, sensitive, or defined by multiple overlapping attributes. In addition, raw reach alone may not provide a fair comparison because some nodes are structurally harder to reach than others. We introduce AURA-IM, a framework for fair influence maximization without predefined groups. AURA-IM does not use observed group memberships or the true numbers of attributes/groups during seed selection. Instead, it uses target-specific reverse-reachable samples to estimate how much service each node can realistically attain under a given seed budget. This attainable service is used to define a normalized opportunity signal. A structural multi-view encoder then learns node representations, and a latent auditor adaptively identifies underserved marginal and intersectional groups. Their risk is modeled using KL-robust utility and upper-tail aggregation, and is jointly optimized with influence spread through an adversarial extragradient–Frank-Wolfe procedure followed by discrete refinement. Experiments on eight real-world networks show that AURA-IM achieves strong influence spread while maintaining competitive observed-group fairness. These results suggest that fairness-aware influence maximization is possible even when protected groups are not available during optimization.