Fast algorithms to improve fair information access in networks
Dennis Robert Windham, Caroline J. Wendt, Alex Crane, Madelyn J Warr,, Freda Shi, Sorelle A. Friedler, Blair D. Sullivan, Aaron Clauset

TL;DR
This paper introduces 10 scalable algorithms for fair influence maximization in networks, significantly improving efficiency while maintaining effectiveness, and provides new benchmarks and evaluation metrics for the task.
Contribution
The authors develop and evaluate 10 new algorithms that do not require probability estimation, along with new evaluation methods and a comprehensive network benchmark.
Findings
Algorithms are about 75-130 times faster than the state-of-the-art.
Meta-learner approach is only 20% less effective but much faster.
Performance exceeds the state-of-the-art on 20% of networks.
Abstract
We consider the problem of selecting seed nodes in a network to maximize the minimum probability of activation under an independent cascade beginning at these seeds. The motivation is to promote fairness by ensuring that even the least advantaged members of the network have good access to information. Our problem can be viewed as a variant of the classic influence maximization objective, but it appears somewhat more difficult to solve: only heuristics are known. Moreover, the scalability of these methods is sharply constrained by the need to repeatedly estimate access probabilities. We design and evaluate a suite of new scalable algorithms which crucially do not require probability estimation. To facilitate comparison with the state-of-the-art, we make three more contributions which may be of broader interest. We introduce a principled method of selecting a pairwise…
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Taxonomy
TopicsCryptography and Data Security · Internet Traffic Analysis and Secure E-voting
