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.

通过分析来自 Flickr 和 Tumblr 的 170M 个自我网络的完整时间演变，我们发现自我网络的演变是爆发性的、社区驱动的，有后续的直径增加、轻微收缩和稳定阶段。 推荐系统有利于流行和连接紧密的节点，限制了直径的展开。 推荐系统引入的偏差促进了邻居选择的全局多样性。最后，我们通过两个链接预测实验展示了如何利用我们分析的见解来提高社交推荐系统的效果。