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

## TL;DR

This study analyzes how link recommendations influence the growth and diversity of ego-networks in social media, revealing their bursty, community-driven evolution and potential to foster diversity through bias.

## Contribution

It provides the first comprehensive analysis of ego-network evolution in social media, highlighting the impact of recommendations on network dynamics and diversity.

## Key findings

- Recommendations favor popular nodes, limiting diameter growth.
- Bias from recommendations can foster global diversity.
- Insights improve social recommender system effectiveness.

## Abstract

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.