Information Exchange and Learning Dynamics over Weakly-Connected Adaptive Networks

TL;DR

This paper explores how weakly-connected adaptive networks influence information flow and learning dynamics, revealing dependencies, vulnerabilities, and potential benefits in reducing outlier effects.

Contribution

It uncovers the impact of weak connectivity on information exchange, dependency structures, and robustness in adaptive networks, highlighting the importance of network topology and adaptation.

Findings

Weak connectivity can mask local information and create leader-follower dynamics.

Strong connectivity and adaptive weights improve uniform learning performance.

Weak connectivity can reduce the influence of outlier data.

Abstract

The paper examines the learning mechanism of adaptive agents over weakly-connected graphs and reveals an interesting behavior on how information flows through such topologies. The results clarify how asymmetries in the exchange of data can mask local information at certain agents and make them totally dependent on other agents. A leader-follower relationship develops with the performance of some agents being fully determined by the performance of other agents that are outside their domain of influence. This scenario can arise, for example, due to intruder attacks by malicious agents or as the result of failures by some critical links. The findings in this work help explain why strong-connectivity of the network topology, adaptation of the combination weights, and clustering of agents are important ingredients to equalize the learning abilities of all agents against such disturbances. The results also clarify how weak-connectivity can be helpful in reducing the effect of outlier data on learning performance.