IEEE

Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning

Abstract: Unsupervised graph-structure learning (GSL) which aims to learn an effective graph structure applied to arbitrary downstream tasks by data itself without any labels’ guidance, has recently ...
C&EN

End-to-End Modeling of Reaction Field Energy Using Data-Driven Geometric Graph Neural Networks

Electrostatic interactions are fundamental to the structure, dynamics, and function of biomolecules, with broad applications in protein–ligand binding, enzymatic catalysis, and nucleic acid regulation ...
C&EN

Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...
IEEE

Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics

Abstract: The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, ...
Scientific Research Publishing

A Maritime Document Knowledge Graph Construction Method Based on Conceptual Proximity Relations ()

1 COSCO Shipping Technology Co., Ltd., Shanghai, China. 2 COSCO Shipping Specialized Carriers Co., Ltd., Guangzhou, China. The cost and strict input format requirements of GraphRAG make it less ...
InfoQ

Modeling Complex Data Structure in Golang Using Pointers, References, and Reverse Indices

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...
marktechpost

GORAM: A Graph-Oriented Data Structure that Enables Efficient Ego-Centric Queries on Federated Graphs with Strong Privacy Guarantees

Ego-centric searches are essential in many applications, from financial fraud detection to social network research, because they concentrate on a single vertex and its immediate neighbors. These ...
Frontiers

Maize yield prediction with trait-missing data via bipartite graph neural network

The timely and accurate prediction of maize (Zea mays L.) yields prior to harvest is critical for food security and agricultural policy development. Currently, many researchers are using machine ...
marktechpost

SFR-GNN: A Novel Graph Neural Networks (GNN) Model that Employs an ‘Attribute Pre-Training and Structure Fine-Tuning’ Strategy to Achieve Robustness Against Structural Attacks

Graph Neural Networks (GNNs) have emerged as the leading approach for graph learning tasks across various domains, including recommender systems, social networks, and bioinformatics. However, GNNs ...