Delayed and Indirect Impacts of Link Recommendations

• URL: http://arxiv.org/abs/2303.09700v1
• Date: Fri, 17 Mar 2023 00:09:19 GMT
• Title: Delayed and Indirect Impacts of Link Recommendations
• Authors: Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei
• Abstract summary: We study the impacts of recommendations on social networks in dynamic settings. We find that link recommendations have surprising delayed and indirect effects on the structural properties of networks. We show that, in counterfactual simulations, removing the indirect effects of link recommendations can make the network trend faster toward what it would have been under natural growth dynamics.
• Score: 23.583662580148133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The impacts of link recommendations on social networks are challenging to evaluate, and so far they have been studied in limited settings. Observational studies are restricted in the kinds of causal questions they can answer and naive A/B tests often lead to biased evaluations due to unaccounted network interference. Furthermore, evaluations in simulation settings are often limited to static network models that do not take into account the potential feedback loops between link recommendation and organic network evolution. To this end, we study the impacts of recommendations on social networks in dynamic settings. Adopting a simulation-based approach, we consider an explicit dynamic formation model -- an extension of the celebrated Jackson-Rogers model -- and investigate how link recommendations affect network evolution over time. Empirically, we find that link recommendations have surprising delayed and indirect effects on the structural properties of networks. Specifically, we find that link recommendations can exhibit considerably different impacts in the immediate term and in the long term. For instance, we observe that friend-of-friend recommendations can have an immediate effect in decreasing degree inequality, but in the long term, they can make the degree distribution substantially more unequal. Moreover, we show that the effects of recommendations can persist in networks, in part due to their indirect impacts on natural dynamics even after recommendations are turned off. We show that, in counterfactual simulations, removing the indirect effects of link recommendations can make the network trend faster toward what it would have been under natural growth dynamics.

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