  The proliferation of rumors on social media platforms during significant
events, such as the US elections and the COVID-19 pandemic, has a profound
impact on social stability and public health. Existing approaches for rumor
detection primarily rely on propagation graphs to enhance model effectiveness.
However, the presence of noisy and irrelevant structures during the propagation
process limits the efficacy of these approaches. To tackle this issue,
techniques such as weight adjustment and data augmentation have been proposed.
However, these techniques heavily depend on rich original propagation
structures, thus hindering performance when dealing with rumors that lack
sufficient propagation information in the early propagation stages. In this
paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement
learning-based rumor detection framework that generates contextually coherent
and informative propagation patterns for events with insufficient topology
information, while also identifies indicative substructures for events with
redundant and noisy propagation structures. KPG consists of two key components:
the Candidate Response Generator (CRG) and the Ending Node Selector (ENS). CRG
learns the latent distribution from refined propagation patterns, filtering out
noise and generating new candidates for ENS. Simultaneously, ENS identifies the
most influential substructures within propagation graphs and generates training
data for CRG. Moreover, we introduce an end-to-end framework that utilizes
rewards to guide the entire training process via a pre-trained graph neural
network. Extensive experiments conducted on four datasets demonstrate the
superiority of our KPG compared to the state-of-the-art approaches.
