Cemri, MertBordignon, V.Kayaalp, M.Shumovskaia, V.Sayed, A. H.2024-03-112024-03-112023-06-041520-6149https://hdl.handle.net/11693/114506Conference Name: 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023Date of Conference: 4 -10 June 2023Social learning algorithms provide a model for the formation and propagation of opinions over social networks. However, most studies focus on the case in which agents share their information synchronously over regular intervals. In this work, we analyze belief convergence and steady-state learning performance for both traditional and adaptive formulations of social learning under asynchronous behavior by the agents, where some of the agents may decide to abstain from sharing any information with the network at some time instants. We also show how to recover the underlying graph topology from observations of the asynchronous network behavior.enCC BY-NC-NDhttps://creativecommons.org/licenses/by-nc-nd/4.0/Adaptive social learningAsynchronous updatesGraph learningSocial learningConference Paper10.1109/ICASSP49357.2023.10096238