Comments (1)
Thanks! Indeed, larger graphs may obviously take more time. The Leiden algorithm is one of the fastest algorithms available, so it won't be easy to find alternative. However, you might be interested in using the implementation in igraph itself: https://python.igraph.org/en/stable/api/igraph.Graph.html#community_leiden. That has more limited capabilities, but should be faster.
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Related Issues (20)
- How to cite leidenalg? HOT 3
- python Segmentation fault: 11 when running the partition function of leidenalg HOT 4
- Memory Error with Clustering with Leiden algorithm matrix - When to use matrix vs igraph method? HOT 8
- find_partition_temporal order of list of membership HOT 1
- Get different result after upgrading to 0.10.1 HOT 2
- Problems installing leidenalg on a remote cluster HOT 4
- Release supporting python-igraph 0.11
- Assigning new nodes to existing communities HOT 4
- Modularity (Q value) for each cluster HOT 3
- Weighted CPM clustering takes much longer when scale of weights is higher HOT 1
- resolution_profile/quality() for temporal communties HOT 1
- Time Slices to Layers Returns Uniform Account Count Across All Layers, HOT 1
- A bunch of strings are broken HOT 1
- Disconnected communities with ModularityVertexPartition on very large graphs HOT 3
- Dendrogram retrieval? HOT 2
- Error while using Leiden algorithm in Seurat HOT 1
- Error with find_partition_multiplex HOT 1
- find_partition_multiplex HOT 4
- Links to igraph documentation should be updated
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