Paper Info
Title
Evolution of Ego-networks in Social Media with Link Recommendations.
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.
Year
DOI
Venue
2017
10.1145/3018661.3018733
WSDM
Keywords
DocType
Volume
Network evolution, ego-networks, link recommendation, groups, communities, social media, Tumblr, Flickr
Conference
abs/1702.01398
Citations 
PageRank 
References 
3
0.37
40
Authors
2
Name
Order
Citations
PageRank
Luca Maria Aiello171344.77
Nicola Barbieri251129.53