However, domain-specific approaches for extracting knowledge graph representations from semantic information remain limited. In this paper, we develop a natural language processing (NLP) approach to ...

DyNoPy generates a graph representation of the protein structure that captures the couplings between amino acid residues contributing to the functional dynamics of the protein. Residues are ...

However, we observe that the aggregation of node information in multilayer graph autoencoder (GAE) is prone to deviation, especially when edges or node attributes are randomly perturbed. To this end, ...

Abstract: Graph Neural Networks (GNNs) show great power in Knowledge Graph Completion (KGC) as they can handle nonEuclidean graph structures and do not depend on the specific shape or topology of the ...

Subgraph-conditioned Graph Information Bottleneck (S-CGIB) is a novel architecture for pre-training Graph Neural Networks in molecular property prediction and developed by NS Lab, CUK based on pure ...

Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity. --Judea Pearl Graph Causal Learning is an emerging research area and it can be widely ...