Information Freshness in Dynamic Gossip Networks

2025-04-25

AI Summary

This paper investigates the evolution of information freshness in dynamic-topology networks. We consider an $n$-node gossip network whose topology switches between two configurations according to a two-state continuous-time Markov chain (CTMC), and adopt version age as the metric for freshness. First, we establish a critical relationship between the CTMC switching rate and the static version ages: when the switching rate exceeds the version age under the faster topology, the network-wide average version age asymptotically converges to the lower bound of that faster topology; otherwise, the slower topology dominates performance. Second, we identify that the behavior of a small subset of nodes can significantly degrade global freshness, leading us to formalize the “canonical node set” concept and quantify its performance degradation bounds under both fast and slow switching regimes. Our results provide a novel theoretical framework and fundamental design principles for analyzing information timeliness in dynamic networks.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We consider a source that shares updates with a network of $n$ gossiping nodes. The network's topology switches between two arbitrary topologies, with switching governed by a two-state continuous time Markov chain (CTMC) process. Information freshness is well-understood for static networks. This work evaluates the impact of time-varying connections on information freshness. In order to quantify the freshness of information, we use the version age of information metric. If the two networks have static long-term average version ages of $f_1(n)$ and $f_2(n)$ with $f_1(n) ll f_2(n)$, then the version age of the varying-topologies network is related to $f_1(n)$, $f_2(n)$, and the transition rates in the CTMC. If the transition rates in the CTMC are faster than $f_1(n)$, the average version age of the varying-topologies network is $f_1(n)$. Further, we observe that the behavior of a vanishingly small fraction of nodes can severely impact the long-term average version age of a network in a negative way. This motivates the definition of a typical set of nodes in the network. We evaluate the impact of fast and slow CTMC transition rates on the typical set of nodes.

Problem

• Evaluates impact of time-varying network topology on information freshness.
• Quantifies freshness using version age metric in dynamic gossip networks.
• Analyzes effect of CTMC transition rates on typical node sets.

Innovation

• Uses version age metric for freshness
• Analyzes two-state CTMC network switching
• Defines typical node set impact