GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion

arXiv:2403.00014v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.ai cs.lg cs.si data detection domain encoding fusion graphs identification networks neural networks nodes paper performance positional encoding robust rumor solutions type user data via