![]() ![]() There is likely to be one that is similar to your data or your problem (if not, let us know). The numerous detailed and narrated examples are a good way to get started with StellarGraph. It is thus also easy to install with pip or Anaconda. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. It is thus user-friendly, modular and extensible. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. graphs with or without data associated with nodes.knowledge graphs (extreme heterogeneous graphs with thousands of types of edges).heterogeneous (with more than one type of nodes and/or links).homogeneous (with nodes and links of one type),.StellarGraph supports analysis of many kinds of graphs: For example, a graph can contain people as nodes and friendships between them as links, with data like a person's age and the date a friendship was established. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. Interpretation of node classification.Classification and attribute inference of nodes or edges.Representation learning for nodes and edges, to be used for visualisation and various downstream machine learning tasks.It can solve many machine learning tasks: The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. Install StellarGraph from GitHub source.Install StellarGraph in Anaconda Python. ![]() StellarGraph is a Python library for machine learning on graphs and networks. ![]()
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