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

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

On the 19th of February 2025, M.Sc. Andreas Grigorjew defends his PhD thesis on Algorithms and Graph Structures for Splitting Network Flows, in Theory and Practice. The thesis is related to research ...

What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...

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 ...

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 ...

ACM, the Association for Computing Machinery, has named 56 Distinguished Members for their impact in the field.

The experimental results show that the performance of the LGNN algorithm ... using graph convolution and activation function, and the embedding in each class is reasonably dispersed to reflect the ...

Quantum algorithms and automata theory are rapidly evolving fields that explore the intersection of quantum computing and formal language theory. Recent research has focused on developing quantum ...