  In social recommender systems, it is crucial that the recommendation models
provide equitable visibility for different demographic groups, such as gender
or race. Most existing research has addressed this problem by only studying
individual static snapshots of networks that typically change over time. To
address this gap, we study the evolution of recommendation fairness over time
and its relation to dynamic network properties. We examine three real-world
dynamic networks by evaluating the fairness of six recommendation algorithms
and analyzing the association between fairness and network properties over
time. We further study how interventions on network properties influence
fairness by examining counterfactual scenarios with alternative evolution
outcomes and differing network properties. Our results on empirical datasets
suggest that recommendation fairness improves over time, regardless of the
recommendation method. We also find that two network properties, minority
ratio, and homophily ratio, exhibit stable correlations with fairness over
time. Our counterfactual study further suggests that an extreme homophily ratio
potentially contributes to unfair recommendations even with a balanced minority
ratio. Our work provides insights into the evolution of fairness within dynamic
networks in social science. We believe that our findings will help system
operators and policymakers to better comprehend the implications of temporal
changes and interventions targeting fairness in social networks.
