Unlock Hidden Insights with Neo4j and n8n : The Future of Knowledge Graphs

Learn how Neo4j and n8n simplify knowledge graphs for smarter data insights. Build AI-driven graphs for customer data and document navigation ...

Machine learning workflow enables faster, more reliable organic crystal structure prediction

Prediction of crystal structures of organic molecules is a critical task in many industries, especially in pharmaceuticals ...
IEEE

Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...
C&EN

RIGR: Resonance-Invariant Graph Representation for Molecular Property Prediction

Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules ...
IEEE

AGSEI: Adaptive Graph Structure Estimation With Long-Tail Distributed Implicit Graphs

Abstract: Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, ...