Characterizing Information Accuracy in Timeliness-Based Gossip Networks
2026-03-02 • Information Theory
Information TheoryNetworking and Internet ArchitectureSocial and Information Networks
AI summaryⓘ
The authors study how accurate information is spread in a network where many devices share updates about a changing source. Each device gets updates directly from the source and also from other devices through a process called gossiping, but only accepts information if it is fresher than what they already have. They create two ways to measure accuracy: one looking at how many devices have correct info on average, and another focusing on whether the freshest update is accurate. Using math models, they analyze these accuracy measures for different types of changing sources and figure out how much accuracy comes from direct updates versus gossip. They support their results with numerical examples.
continuous-time Markov chaingossip networkinformation freshnessstatus updatesstochastic hybrid systemssteady-statematrix recursionaccuracy metricsmulti-state sourcenumerical analysis
Authors
Emirhan Tekez, Melih Bastopcu, Sinan Gezici
Abstract
We investigate information accuracy in timeliness-based gossip networks where the source evolves according to a continuous-time Markov chain (CTMC) with $M$ states and disseminates status updates to a network of $n$ nodes. In addition to direct source updates, nodes exchange their locally stored packets via gossip and accept incoming packets solely based on whether the incoming packet is fresher than their local copy. As a result, a node can possess the freshest packet in the network while still not having the current source state. To quantify the amount of accurate information flowing in the network under such a gossiping scheme, we introduce two accuracy metrics, average accuracy, defined as the expected fraction of nodes carrying accurate information in any given subset, and freshness-based accuracy, defined as the accuracy of the freshest node in any given subset. Using a stochastic hybrid systems (SHS) framework, we first derive steady-state balance equations and obtain matrix-valued recursions that characterize these metrics in fully connected gossip networks under binary CTMCs. We then extend our analysis to the general multi-state information source using a joint CTMC approach. Finally, we quantify the fraction of nodes whose information is accurate due to direct source pushes versus gossip exchanges. We verify our findings with numerical analyses and provide asymptotic insights.