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E. J. Newman, « Modularity and community structure in networks », Proc. Natl. Acad. Sci. USA, vol. 103, no 23, 2006, p. 8577–8582 https://dx.doi.org/10.1073%2Fpnas.0601602103\n\n> Newman, M. E. J. « Community detection in networks: Modularity optimization and maximum likelihood are equivalent ». Physical Review E, vol. 94, no 5, novembre 2016, p. 052315. arXiv.org, doi:10.1103/PhysRevE.94.052315. https://arxiv.org/pdf/1606.02319.pdf\n\n> Blondel, Vincent D., et al. « Fast unfolding of communities in large networks ». Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no 10, octobre 2008, p. P10008. DOI.org (Crossref), doi:10.1088/1742-5468/2008/10/P10008. https://arxiv.org/pdf/0803.0476.pdf\n\n> Nicolas Dugué, Anthony Perez. Directed Louvain: maximizing modularity in directed networks. [Research Report] Université d’Orléans. 2015. hal-01231784 https://hal.archives-ouvertes.fr/hal-01231784\n\n> R. Lambiotte, J.-C. Delvenne and M. Barahona. Laplacian Dynamics and Multiscale Modular Structure in Networks, doi:10.1109/TNSE.2015.2391998. https://arxiv.org/abs/0812.1770\n\n> Traag, V. A., et al. « From Louvain to Leiden: Guaranteeing Well-Connected Communities ». Scientific Reports, vol. 9, no 1, décembre 2019, p. 5233. DOI.org (Crossref), doi:10.1038/s41598-019-41695-z. https://arxiv.org/abs/1810.08473\n\n## Installation\n\n```\nnpm install graphology-communities-louvain\n```\n\n## Usage\n\nRuns the Louvain algorithm to detect communities in the given graph. It works both for undirected & directed graph by using the relevant modularity computations.\n\nThis function also works on multi graphs but won't work with mixed graph as it is not trivial to adapt modularity to this case. As such, if you need to process a true mixed graph (this function will correctly handle mixed graphs containing only directed or undirected edges), cast your graph as either directed or undirected using [graphology-operators](https://github.com/graphology/graphology-operators) dedicated functions.\n\nNote also that this algorithm's run time is bounded by the number of edges in your graph. As such, it might be preferable, in some cases, to cast your multi graph as a simple one using [graphology-operators](https://github.com/graphology/graphology-operators) functions for better performance.\n\nNote that the community labels are returned as an integer range from 0 to n.\n\n```js\nimport louvain from 'graphology-communities-louvain';\n\n// To retrieve the partition\nconst communities = louvain(graph);\n\n// To directly assign communities as a node attribute\nlouvain.assign(graph);\n\n// If you need to pass custom options\nlouvain.assign(graph, {\n  resolution: 0.8\n});\n\n// If you want to return some details about the algorithm's execution\nvar details = louvain.detailed(graph);\n\n// If you want to ignore your graph's weights\nlouvain.assign(graph, {getEdgeWeight: null});\n```\n\n_Arguments_\n\n- **graph** _Graph_: target graph.\n- **options** _?object_: options:\n  - **getEdgeWeight** _?string\\|function_ [`weight`]: name of the edges' weight attribute or getter function.\n  - **nodeCommunityAttribute** _?string_ [`community`]: name of the community attribute.\n  - **fastLocalMoves** _?boolean_ [`true`]: whether to use a well-known optimization relying on a queue set to traverse the graph more efficiently.\n  - **randomWalk** _?boolean_ [`true`]: whether to traverse the graph randomly.\n  - **resolution** _?number_ [`1`]: resolution parameter. An increased resolution should produce more communities.\n  - **rng** _?function_ [`Math.random`]: RNG function to use for `randomWalk`. Useful if you need to seed your rng using, for instance, [seedrandom](https://www.npmjs.com/package/seedrandom).\n\n_Detailed Output_\n\n- **communities** [`object`]: partition of the graph.\n- **count** [`number`]: number of communities in the partition.\n- **deltaComputations** [`number`]: number of delta computations that were run to produce the partition.\n- **dendrogram** [`array`]: array of partitions.\n- **modularity** [`number`]: final modularity of the graph given the found partition.\n- **moves** [`array`]: array of array of number of moves if `fastLocalMoves` was false or array of number of moves if `fastLocalMoves` was true.\n- **nodesVisited** [`number`]: number of times nodes were visited.\n\n## Benchmark\n\nTo run the benchmark:\n\n```\nnpm install --no-save ngraph.louvain.native\nnode benchmark/comparison.js\n```\n\n```\nClustered Undirected graph with 1000 nodes and 9724 edges.\n\ngraphology undirected1000: 52.732ms\nCommunities 8\nModularity 0.42944314393593924\n\njlouvain undirected1000: 2368.053ms\nCommunities 8\nModularity 0.4302331134880074\n\nngraph.louvain undirected1000: 71.108ms\nCommunities 8\n\nngraph.louvain.native undirected1000: 39.185ms\nCommunities 7\n\n---\n\nEuroSIS Directed graph with 1285 nodes and 7524 edges.\n\ngraphology euroSis: 30.809ms\nCommunities 19\nModularity 0.7375260763995757\n\njlouvain euroSis: 1310.008ms\nCommunities 23\nModularity 0.7376116434498033\n\nngraph euroSis: 38.262ms\nCommunities 16\n\nngraph.native euroSis: 20.018ms\nCommunities 16\n\n---\n\nBig Undirected graph with 50000 nodes and 994713 edges.\n\ngraphology bigGraph: 937.942ms\nCommunities 43\nModularity 0.481431448861252\n\njLouvain is too slow...\n\nngraph bigGraph: 7783.050ms\nCommunities 44\n\nngraph.native bigGraph: 8415.692ms\nCommunities 1\n```\n","readmeFilename":"README.md"}