Unlocking Diagonal Spreads: A Strategy Guide for Options Trading

Learn how diagonal spreads offer strategic flexibility in options trading by combining varied strike prices and expiration ...
C&EN

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material ...
IEEE

Enforced Block Diagonal Graph Learning for Multikernel Clustering

Abstract: The existing multikernel graph clustering (MKGC) methods have emerged with notable success on nonlinear clustering tasks since the graph learning can effectively capture graph structure ...
C&EN

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...
Frontiers

Efficient graph convolutional networks for seizure prediction using scalp EEG

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy ...
Frontiers

Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network

As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much ...
IEEE

Kron Reduction of Graphs With Applications to Electrical Networks

Abstract: Consider a weighted undirected graph and its corresponding Laplacian matrix, possibly augmented with additional diagonal elements corresponding to self-loops. The Kron reduction of this ...
GitHub

TeMP: Temporal Message Passing Network for Temporal Knowledge Graph Completion

Update: you can now download the available trained model here. The hyperparameter configuration are both suggested by the folder name, and detailed in the config.json in each checkpoint folder.