The author introduces a novel framework, GIN-SD, to address the challenge of rumor source detection in graphs with incomplete nodes. By leveraging positional encoding and attentive fusion, the model aims to distinguish incomplete nodes and focus on those with higher information transmission capacity.
Proposing GIN-SD framework for rumor source detection in graphs with incomplete nodes.
Effective maximum-likelihood estimation for identifying the source of contagion in networks.
Probabilistic approach for rumor source detection using maximum likelihood estimation in spreading models.