  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 among the agents. To account for drifting conditions in the
environment, this work adopts an adaptive social learning strategy, which is
able to track variations in the underlying signal statistics. Among other
results, we propose a technique that addresses questions of explainability and
interpretability of the results when the graph is hidden. Given observations of
the evolution of the beliefs over time, we aim to infer the underlying graph
topology, discover pairwise influences between the agents, and identify
significant trajectories in the network. The proposed framework is online in
nature and can adapt dynamically to changes in the graph topology or the true
hypothesis.
