Graphs are among the most widely-used data structures in machine learning. Their power comes from the flexibility of capturing relations (edges) of collections of entities (nodes) which arise in a ...

With graph technology, companies can create a digital representation of a system ... Examine the names of the data structures you are trying to analyze. Names like “network,” “tree ...

Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

The strategy for encoding Petri nets as chained linear lists involves two structures: one for PList placements and one for TList transitions. This is the orthogonal representation of chained linear ...

The representation of the relationships among ... focusing on a specific data structure: graphs. What is the optimal way to go through a number of bridges? The history of graph theory is linked ...