IEEE

Maximizing k-Terminal Network Reliability in Some Sparse Graphs

Abstract: k-terminal network reliability is the probability that k terminal vertices are connected given that edges in the network fail independently while vertices do not fail. It depends on the ...
GitHub

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from ...
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 ...
IEEE

k* Distribution: Evaluating the Latent Space of Deep Neural Networks Using Local Neighborhood Analysis

Abstract: Most examinations of neural networks’ learned latent spaces typically employ dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) or uniform ...
GitHub

Reconstruct haplotypes and produce genealogy graphs from population data.

Download and run the standalone executables without installing Python. Vences M. et al. (2021): iTaxoTools 0.1: Kickstarting a specimen-based software toolkit for taxonomists. - Megataxa 6: 77-92.