# Evolution of Ego-networks in Social Media with Link Recommendations

arXiv: https://arxiv.org/abs/1702.01398 · HTML: https://mlsanity.com/paper/1702.01398/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks](https://mlsanity.com/paper/2109.09190/) (2021)
- [Delayed and Indirect Impacts of Link Recommendations](https://mlsanity.com/paper/2303.09700/) (2023)

## Abstract (arXiv)

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.