_:n3002dc4b209f4df0b0fe23e58c187225xb0
<http://schema.org/description>
"We study the problem of learning influence functions under incomplete\nobservations of node activations. Incomplete observations are a major concern\nas most (online and real-world) social networks are not fully observable. We\nestablish both proper and improper PAC learnability of influence functions\nunder randomly missing observations. Proper PAC learnability under the\nDiscrete-Time Linear Threshold (DLT) and Discrete-Time Independent Cascade\n(DIC) models is established by reducing incomplete observations to complete\nobservations in a modified graph. Our improper PAC learnability result applies\nfor the DLT and DIC models as well as the Continuous-Time Independent Cascade\n(CIC) model. It is based on a parametrization in terms of reachability\nfeatures, and also gives rise to an efficient and practical heuristic.\nExperiments on synthetic and real-world datasets demonstrate the ability of our\nmethod to compensate even for a fairly large fraction of missing observations."
<https://deepai.org/publication/learning-influence-functions-from-incomplete-observations>