A comprehensive generalization of the Friendship Paradox to weights and attributes
Anna Evtushenko, Jon Kleinberg

TL;DR
This paper unifies and extends the Friendship Paradox to weighted edges and numeric node attributes in undirected graphs, providing theoretical insights, practical rules, and empirical validation on real and synthetic networks.
Contribution
It offers a comprehensive framework that generalizes the Friendship Paradox to weighted edges and attributes, clarifying when these extensions hold or fail.
Findings
Original and weighted friendship paradoxes hold universally.
Attribute-based extensions fail about 50% of the time with random attributes.
Correlation rules can predict when the paradox applies.
Abstract
The Friendship Paradox is a simple and powerful statement about node degrees in a graph (Feld 1991). However, it only applies to undirected graphs with no edge weights, and the only node characteristic it concerns is degree. Since many social networks are more complex than that, it is useful to generalize this phenomenon, if possible, and a number of papers have proposed different generalizations. Here, we unify these generalizations in a common framework, retaining the focus on undirected graphs and allowing for weighted edges and for numeric node attributes other than degree to be considered, since this extension allows for a clean characterization and links to the original concepts most naturally. While the original Friendship Paradox and the Weighted Friendship Paradox hold for all graphs, considering non-degree attributes actually makes the extensions fail around 50% of the time,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
