Online Graph Learning from Social Interactions

TL;DR

This paper introduces an online algorithm to infer the underlying social network graph from observed opinion dynamics, enabling dynamic adaptation to changes in social influence and information flow.

Contribution

It presents a novel online method for inverse social learning, identifying influence structures from belief evolution data in social networks.

Findings

Successfully infers influence relationships between agents.

Adapts to changes in network topology over time.

Provides insights into information flow and influence strength.

Abstract

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information and relative influence between pairs of agents. For a given graph topology, these algorithms allow for the prediction of formed opinions. In this work, we study the inverse problem. Given a social learning model and observations of the evolution of beliefs over time, we aim at identifying the underlying graph topology. The learned graph allows for the inference of pairwise influence between agents, the overall influence agents have over the behavior of the network, as well as the flow of information through the social network. The proposed algorithm is online in nature and can adapt dynamically to changes in the graph topology or the true hypothesis.