Delayed and Indirect Impacts of Link Recommendations

Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei

Abstract: The impacts of link recommendations on social networks are challenging to evaluate, due to feedback loops between algorithmic recommendations and underlying network dynamics. Observational studies have limitations in answering causal questions; naive A/B experiments often result in biased evaluations due to unaccounted network interference and finally, existing simulations primarily employ static network models that do not take into account dynamics. Departing from existing approaches, we employ simulations to study dynamic impacts of link recommendations. Specifically, we propose an extension to the Jackson-Rogers network evolution model and investigate how link recommendations affect network evolution over time. Our experiments demonstrate that link recommendations can have surprising delayed and indirect effects on the structural properties of networks. Effects of recommendations vary in the short-term and long-term, such as the immediate reduction in degree inequality but eventual increase in degree inequality through friend-of-friend recommendations. Furthermore, even after recommendations are discontinued, their impacts can persist in the network, in part by altering natural network evolution dynamics. These results provide valuable insights into the interplay between algorithmic interventions and natural network dynamics and highlight the limitations of current evaluation paradigms.