Python3 implementation of the Leiden algorithm in C++


This package implements the Leiden algorithm in C++ and exposes it to Python. It relies on igraph for it to function. Besides the relative flexibility of the implementation, it also scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The core function is find_partition which finds the optimal partition using the Leiden algorithm, which is an extension of the Louvain algorithm for a number of different methods. The methods currently implemented are

 1. modularity,
 2. Reichardt and Bornholdt's model using the configuration null model
    and the Erdös-Rényi null model,
 3. the Constant Potts model (CPM),
 4. Significance and finally
 5. Surprise.

In addition, it supports multiplex partition optimisation allowing community detection on for example negative links or multiple time slices. There is the possibility of only partially optimising a partition, so that some community assignments remain fixed. It also provides some support for community detection on bipartite graphs.

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