How can I optimize memory usage in NetworkX?
An example of an AI chatbot conversation about optimizing memory usage in NetworkX.
How can I optimize memory usage in NetworkX?
To optimize memory usage in NetworkX, consider using sparse data structures, like networkx.Graph with adjacency_list.
What are sparse data structures?
Sparse data structures store only non-zero elements, which can significantly reduce memory usage for large graphs.
Can you give me an example of using a sparse structure in NetworkX?
Sure! You can use networkx.from_scipy_sparse_matrix to create a graph from a sparse matrix, which saves memory.
Are there any other tips for reducing memory usage?
Yes! You can also remove unnecessary nodes and edges, and use networkx's built-in functions to manage graph size.
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