Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different ...

GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.

A Java-based project that visualizes a traffic management system using graph structures and JavaFX. It demonstrates node and edge representation for traffic flow between locations, providing an ...

They require a knowledge graph. How does the journey to a knowledge graph start with unstructured data—such as text, images, and other media? The evolution of web search engines offers an ...