Comments (4)
Thank you. This is what I did in the end. I think I initially misunderstood how this worked. My concern was that new nodes might be forced into partitions where they did not really belong. But now i realise new nodes will only form part of an existing cluster if that is better than creating a new one.
This has been a really neat module.
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Have you already considered using "Fixed Nodes"?
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Thank you for responding. That's what I'm trying.
For a multiplex graph, I guess it doesn't matter which graph you choose to be G (in the "Fixed Nodes" example), as the partitions are the same across the layers?
As for new nodes, are completely unconnected nodes disregarded? And then you can control the sensitivity for assigning new nodes with the resolution parameter?
Or would you assign all new (connected) nodes to a partition then exclude those below a certain modularity as a separate step?
from leidenalg.
For a multiplex graph, I guess it doesn't matter which graph you choose to be G (in the "Fixed Nodes" example), as the partitions are the same across the layers?
Yes, indeed. Note that the argument is_membership_fixed
is an argument of optimise_partition_multiplex
, not of any particular partition of any layer/graph.
As for new nodes, are completely unconnected nodes disregarded? And then you can control the sensitivity for assigning new nodes with the resolution parameter?
New nodes are not disregarded, they are simply not fixed. You should make sure that they are assigned some cluster when you create a new graph and initial partition of that new graph.
Or would you assign all new (connected) nodes to a partition then exclude those below a certain modularity as a separate step?
I am not entirely sure if I understand what you mean here. Personally, I would just assign each new node simply to a new community, such that each new node is a singleton (and unrelated to any existing community). Then by keeping the existing ones fixed, the algorithm will automatically find a way to assign the new nodes to the existing communities (or keep them separate if that is better).
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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
- Can't Finished running on million nodes and one hundred million edges data HOT 1
- Release supporting python-igraph 0.11
- 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 3
- 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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