Category-Based Routing in Social Networks: Membership Dimension and the Small-World Phenomenon (Short)
David Eppstein, Michael T. Goodrich, Maarten L\"offler, Darren Strash,, Lowell Trott

TL;DR
This paper introduces the membership dimension, a measure of cognitive load in social networks, and demonstrates its connection to the small-world phenomenon, showing that networks with small-world properties can support efficient greedy routing with low cognitive effort.
Contribution
The paper defines the membership dimension and proves its equivalence to the small-world property for enabling low-cognitive-load greedy routing in social networks.
Findings
Networks with small-world properties support low membership dimension.
Any connected network can support greedy routing with appropriate categories.
Small membership dimension correlates with the small-world phenomenon.
Abstract
A classic experiment by Milgram shows that individuals can route messages along short paths in social networks, given only simple categorical information about recipients (such as "he is a prominent lawyer in Boston" or "she is a Freshman sociology major at Harvard"). That is, these networks have very short paths between pairs of nodes (the so-called small-world phenomenon); moreover, participants are able to route messages along these paths even though each person is only aware of a small part of the network topology. Some sociologists conjecture that participants in such scenarios use a greedy routing strategy in which they forward messages to acquaintances that have more categories in common with the recipient than they do, and similar strategies have recently been proposed for routing messages in dynamic ad-hoc networks of mobile devices. In this paper, we introduce a network…
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.
Taxonomy
TopicsComplex Network Analysis Techniques · Peer-to-Peer Network Technologies · Opinion Dynamics and Social Influence
