Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

In this talk, I will give a high-level tutorial on graphs of convex sets, with emphasis on their applications in robotics, control, and, more broadly, decision making. Mathematically, a Graph of ...

If you’re like me, you’ve heard plenty of talk about entity SEO and knowledge graphs over the past year. But when it comes to implementation, it’s not always clear which components are worth the ...

Abstract: In this paper, a Mahalanobis Distance-based Graph Attention Network for graph classification, is proposed. In contrast to traditional Graph Attention Networks, the proposed approach learns ...

Federated learning is a classic of privacy-preserving learning, which enables collaborative learning without sharing data. Structured data has become the mainstream of current applications, where ...

ABSTRACT: Let G be a connected graph with vertex set V( G ) . Then the degree resistance distance of G is defined as D R ( G )= ∑ { u,v }⊆V( G ) ( d( u )+d( v ) )R( u,v ) , where d( u ) is the degree ...

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph ...