# Delayed and Indirect Impacts of Link Recommendations

arXiv: https://arxiv.org/abs/2303.09700 · HTML: https://mlsanity.com/paper/2303.09700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [The Social Cost of Strategic Classification](https://mlsanity.com/paper/1808.08460/) (2018)
- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Exposure Inequality in People Recommender Systems: The Long-Term Effects](https://mlsanity.com/paper/2112.08237/) (2021)
- [Cumulative effects of triadic closure and homophily in social networks](https://mlsanity.com/paper/1809.06057/) (2018)
- [Long-term Dynamics of Fairness Intervention in Connection Recommender Systems](https://mlsanity.com/paper/2203.16432/) (2022)
- [Towards Psychologically-Grounded Dynamic Preference Models](https://mlsanity.com/paper/2208.01534/) (2022)
- [Inequality and Inequity in Network-based Ranking and Recommendation Algorithms](https://mlsanity.com/paper/2110.00072/) (2021)
- [Link recommendations: Their impact on network structure and minorities](https://mlsanity.com/paper/2205.06048/) (2022)
- [On the Effect of Triadic Closure on Network Segregation](https://mlsanity.com/paper/2205.13658/) (2022)
- [Evolution of Ego-networks in Social Media with Link Recommendations](https://mlsanity.com/paper/1702.01398/) (2017)
- [Recommending links through influence maximization](https://mlsanity.com/paper/1706.04368/) (2017)
- [On the Impact of Social Media Recommendations on Opinion Consensus](https://mlsanity.com/paper/2204.03299/) (2022)

## Abstract (arXiv)

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.