The content discusses the introduction of GIN-SD, a framework designed for rumor source detection in graphs with incomplete nodes. The paper addresses the challenges posed by incomplete user data and proposes innovative solutions through positional encoding, self-attention mechanisms, and class balancing. Extensive experiments validate the effectiveness of GIN-SD compared to state-of-the-art methods.
The content covers various aspects related to source detection in graphs, including the importance of distinguishing incomplete nodes, focusing on high-information transmission nodes, and addressing prediction bias. The proposed framework integrates positional embedding and attentive fusion modules to enhance source detection accuracy. Experimental results demonstrate significant improvements over existing methods.
Key points include discussing the impact of incomplete nodes on source detection accuracy, introducing a class-balancing mechanism to mitigate prediction bias, and validating the effectiveness of GIN-SD through extensive experiments on real-world datasets.
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by Le Cheng,Pei... kl. arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00014.pdfDybere Forespørgsler