Question 1

How do I install igraph on Windows with GraphML support?

Accepted Answer

Install libxml2 via vcpkg, set environment variables like IGRAPH_CMAKE_EXTRA_ARGS and IGRAPH_EXTRA_LIBRARIES, then run pip install. The README provides detailed steps, but it's more involved than standard pip installations.

Question 2

Is igraph faster than NetworkX for large graphs?

Accepted Answer

Yes, igraph is generally faster for large-scale graph operations due to its optimized C core, while NetworkX is pure Python and better for prototyping. Choose igraph for performance-critical tasks like analyzing social networks or big datasets.

Question 3

How can I visualize graphs using igraph?

Accepted Answer

igraph supports visualization through integrations with libraries like matplotlib, but it requires additional dependencies. Check the documentation for plotting functions and examples, as it's not a primary focus compared to analysis.

Question 4

Does igraph work with Python 3.13?

Accepted Answer

Yes, it aims to support all non-EOL Python versions, currently from 3.9 to 3.13, with continuous integration tests to ensure compatibility, as mentioned in the supported Python versions section.

Question 5

How to link python-igraph to an existing igraph C library?

Accepted Answer

Set IGRAPH_USE_PKG_CONFIG=1 and ensure pkg-config can find igraph, then run pip install. However, this is primarily for package maintainers and may lead to compatibility issues if versions differ.

Question 6

What are the performance benchmarks for igraph?

Accepted Answer

Specific benchmarks aren't provided in the README, but the library is designed for high performance with large graphs. Users should test with their own datasets, as performance can vary based on graph size and operations.

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