An Information-theoretic Model for Knowledge Sharing in Opportunistic Social Networks

TL;DR

This paper introduces an information-theoretic framework to analyze the fundamental performance limits of knowledge sharing in opportunistic social networks, providing insights into optimal policies and potential trade-offs.

Contribution

It presents a novel model for quantifying knowledge sharing performance limits and characterizes these limits across various network topologies and policies.

Findings

Knowledge gain can be bounded and characterized mathematically.

Numerical results validate the model using real smartphone data.

The model highlights key factors influencing knowledge sharing efficiency.

Abstract

In this paper we establish fundamental limits on the performance of knowledge sharing in opportunistic social net- works. In particular, we introduce a novel information-theoretic model to characterize the performance limits of knowledge sharing policies. Towards this objective, we first introduce the notions of knowledge gain and its upper bound, knowledge gain limit, per user. Second, we characterize these quantities for a number of network topologies and sharing policies. This work constitutes a first step towards defining and characterizing the performance limits and trade-offs associated with knowledge sharing in opportunistic social networks. Finally, we present nu- merical results characterizing the cumulative knowledge gain over time and its upper bound, using publicly available smartphone data. The results confirm the key role of the proposed model to motivate future research in this ripe area of research as well as new knowledge sharing policies.