  Source detection in graphs has demonstrated robust efficacy in the domain of
rumor source identification. Although recent solutions have enhanced
performance by leveraging deep neural networks, they often require complete
user data. In this paper, we address a more challenging task, rumor source
detection with incomplete user data, and propose a novel framework, i.e.,
Source Detection in Graphs with Incomplete Nodes via Positional Encoding and
Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach
utilizes a positional embedding module to distinguish nodes that are incomplete
and employs a self-attention mechanism to focus on nodes with greater
information transmission capacity. To mitigate the prediction bias caused by
the significant disparity between the numbers of source and non-source nodes,
we also introduce a class-balancing mechanism. Extensive experiments validate
the effectiveness of GIN-SD and its superiority to state-of-the-art methods.
