Graphs are used to model complex systems ... For example, GNNs can be used for node classification tasks, in which the goal is to assign a label to each node in the graph based on its features ...

Classic Graph Convolutional Networks ... Experiments show that their model outperforms all the baselines in both single- and multi-label node classification tasks in terms of test accuracies.

F or those who enjoy rooting for the underdog, the latest MLPerf benchmark results will disappoint: Nvidia’s GPUs have ...

classification and clustering is beneficial. Graphs are among the most widely-used data structures in machine learning. Their power comes from the flexibility of capturing relations (edges) of ...

The team responsible for BioCypher accomplished precisely this by taking a big corpus of medical research papers, building a large language model around them, and then deriving a knowledge graph ...

That means that we found a way that we can combine the JSON document data model, the graph model, and the key-value model in one database core with one query language." Today ArangoDB is a US ...

Graph theory isn’t enough. The mathematical language for talking about connections, which usually depends on networks — vertices (dots) and edges (lines connecting them) — has been an invaluable way ...