Graph contrastive learning (GCL) enables graph neural networks (GNNs) to learn generalized node features and achieve better performance on downstream tasks. Meanwhile, with the rapid growth of social ...

NVIDIA and ArangoDB introduce a solution to boost NetworkX performance for medium-to-large graphs using RAPIDS cuGraph and ArangoDB. NetworkX, a widely-used Python library for graph analytics, often ...

Learn how call graphs can significantly enhance Python code performance by visualizing function interactions for data analytics.

Currently, community detection in signed networks has become a popular research topic due to the widespread use of signed networks for modeling relationships among entities in the real world. However, ...

Makes use of NetworkX library and PyLab. This creates a fixed graph and determines the shortest path determined by all of the node and edges. We were tasked to do this in any programming language we ...

To improve the situation, google researchers created python_graph as it doesn’t need any other source; hence it is also free from its disadvantages. Control flow graphs are the graphs that show the ...

Graph representations of source code — abstract syntax tree (AST), control-flow graph (CFG), data-flow graphs, etc. — are now commonly employed by machine learning researchers for code research. In ...