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Scalable fair influence maximization
X Rui, Z Wang, J Zhao, L Sun… - Advances in Neural …, 2023 - proceedings.neurips.cc
Given a graph $ G $, a community structure $\mathcal {C} $, and a budget $ k $, the fair
influence maximization problem aims to select a seed set $ S $($| S|\leq k $) that maximizes …
influence maximization problem aims to select a seed set $ S $($| S|\leq k $) that maximizes …
Efficient approximation algorithms for adaptive influence maximization
K Huang, J Tang, K Han, X ** Communities and Optimization Algorithms
L Chen, A Rezaeipanah - Neurocomputing, 2025 - Elsevier
Influence Maximization (IM) is a fundamental problem in social networks, aiming to identify a
small set of seed nodes that maximize the spread of influence. Fair Influence Maximization …
small set of seed nodes that maximize the spread of influence. Fair Influence Maximization …
Better bounds on the adaptivity gap of influence maximization under full-adoption feedback
In the influence maximization (IM) problem, we are given a social network and a budget k,
and we look for a set of k nodes in the network, called seeds, that maximize the expected …
and we look for a set of k nodes in the network, called seeds, that maximize the expected …
On adaptive influence maximization under general feedback models
G Tong, R Wang - IEEE Transactions on Emerging Topics in …, 2020 - ieeexplore.ieee.org
The classic influence maximization problem explores the strategies for deploying cascades
such that the total influence is maximized, and it assumes that the seed nodes that initiate …
such that the total influence is maximized, and it assumes that the seed nodes that initiate …