Jul 2026· International Conference on Big Data Computing Service and Applications· pp. 138-145· 0 citations· 28 references
Abstract
Influence maximization (IM) selects a small set of seed users to maximize expected diffusion in a social network, typically under the Independent Cascade model. Optimizing only global spread can amplify pre-existing structural inequities: some groups (e.g., demographics, communities, or departments) may receive far less exposure than others even when the overall spread is high. We present FIMMOGA++, a fairness-aware influence maximization framework that formulates IM as a multi-objective optimization problem over expected spread and multiple groupfairness objectives. FIMMOGA++ integrates efficient influence estimation via Reverse Influence Sampling (RIS) with a manyobjective genetic algorithm. Our framework returns a Pareto front of seed sets, explicitly exposing the trade-off between diffusion efficiency and fairness. We show that FIMMOGA++ improves fairness metrics (e.g., Max–Min group coverage and inequality) while remaining competitive in terms of spread and runtime relative to standard IM baselines.
RBwA, a memory-efficient and sample-efficient progressive sampling algorithm for IM-PC and a memory-efficient rounding scheme called BwARound for coverage maximization subroutines, which only requires storing one fractional vector and takes maximal feasible steps rather than tiny ε-increments, are proposed.
Qixin Zhang, Qirun Zeng, Hui Lu et al.· Proceedings of the 32nd ACM...· 0 citations
Influence maximization seeks a limited seed set that maximizes diffusion spread. Topology-based rankings are efficient but often ignore finite-horizon dynamics and seed-set redundancy, whereas simulation-assisted greedy methods can be computationally expensive. To balance effectiveness, efficiency, and interpretability...
Tian-Fu Zhang, Bao-Jun Fu· Journal of King Saud Univers...· 0 citations
Influence maximization asks for $k$ seed vertices that maximize the expected spread of a diffusion process in a network. Standard near-optimal-time algorithms based on reverse-reachable sampling achieve a $(1-1/e-\varepsilon)$ approximation, but their expected running-time bounds grow linearly with the seed budget $k$....
: Ranking systems that combine expert scores with crowd votes often cannot observe the crowd-vote signal directly; only the elimination outcomes it produces are recorded. Recovering the latent signal from these censored outcomes is an inverse problem with a vast space of solutions that fit the data equally well. This p...
Zhen-Heng Fu· Academic Journal of Computin...· 0 citations
Motivated by the observation that users in the real world often engage across multiple social networks simultaneously, we study the problem of influence maximization in multilayer social networks (Mlim), aiming to select a small set of nodes that maximizes the total influence spread across all layers. To this end, we i...
Xueqin Chang, Rui-Ze Liu, Qing Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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