Fast algorithms to improve fair information access in networks

Abstract

When information spreads across a network via pairwise sharing, large disparities in information access can arise from the network's structural heterogeneity. Algorithms to improve the fairness of information access seek to maximize the minimum access of a node to information by sequentially selecting new nodes to seed with the spreading information. However, existing algorithms are computationally expensive. Here, we develop and evaluate a set of 10 new scalable algorithms to improve information access in social networks; in order to compare them to the existing state-of-the-art, we introduce both a new performance metric and a new benchmark corpus of networks. Additionally, we investigate the degree to which algorithm performance on minimizing information access gaps can be predicted ahead of time from features of a network's structure. We find that while no algorithm is strictly superior to all others across networks, our new scalable algorithms are competitive with the state-of-the-art and orders of magnitude faster. We introduce a meta-learner approach that learns which of the fast algorithms is best for a specific network and is on average only 20% less effective than the state-of-the-art performance on held-out data, while about 75-130 times faster. Furthermore, on about 20% of networks the meta-learner's performance exceeds the state-of-the-art.