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Affinity Workshop: Women in Machine Learning

Fair Targeted Immunization with Dynamic Influence Maximization

Nicola Neophytou · Golnoosh Farnadi


The argument for targeted immunization has been prevailing since the Covid-19 pandemic. However, sophisticated techniques to identify “superspreaders” for targeted vaccination may lead to inequalities in vaccine distribution and immunity from Covid-19 between social communities. This is particularly poignant in social networks which demonstrate homophily: our tendancy to interact more with those whom we share similar demographics. If our contact networks similarly show that we move in close communities, can we ensure that targeted immunization does not benefit one community over another? Here, we answer this question by applying group fairness constraints, ensuring immunity is balanced among different sub-populations, to an Influence Maximization (IM) task. IM is a technique which identifies the most influential members of a social network, those who are responsible for the greatest spread of e.g. disease or information. Previous works have demonstrated the equivalence of outbreak minimization and IM to detect superspreaders, and shown that networks with homophilic social networks lead to more unbalanced spread of information. Whilst the fair IM problem has been approached from a time-critical perspective, no attempt has yet been made to achieve group fairness on dynamic social networks. Here, we propose a novel method for applying fairness constraints to IM on dynamic and homophilic social networks to detect superspreaders.

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