핵심 개념
Proposing RCoCo for collective link prediction in multiplex networks, leveraging Riemannian spaces for intra- and inter-network behaviors.
초록
The article introduces RCoCo, a novel model for Geometry-aware Collective Link Prediction across Multiplex Network. It addresses challenges in intra- and inter-link prediction, emphasizing the importance of Riemannian spaces. The content covers the abstract, introduction, methodology, challenges, and experimental setups with detailed explanations and insights.
통계
Link prediction studies the probability of future interconnection among nodes.
Most existing works focus on intra-link prediction in a single network or inter-link prediction among networks.
RCoCo proposes a contrastive model for collective link prediction in Riemannian spaces.
Extensive experiments with 14 strong baselines on 8 real-world datasets show the effectiveness of RCoCo.
인용구
"In RCoCo, we design a curvature-aware graph attention network (κ−GAT) to learn informative user representation in Riemannian spaces."
"We propose to study a challenging yet practical problem of Geometry-aware Collective Link Prediction across Multiplex Network."