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"description": "[](https://travis-ci.org/kfoynt/LocalGraphClustering)\n[](https://coveralls.io/github/kfoynt/LocalGraphClustering?branch=master)\n# Local Graph Clustering\n\nLocal Graph Clustering provides\n\n- methods that find local clusters in a given graph without touching the whole graph\n- methods that improve a given cluster\n- methods for global graph partitioning\n- tools to compute [Network Community Profiles](http://www.tandfonline.com/doi/abs/10.1080/15427951.2009.10129177)\n- scalable graph analytics on your laptop\n\nThe current version is 0.4.4 and it is appropriate for experts and intermediates. Contact information for any questions and feedback is given below.\n\n### Authors\n\n- [Kimon Fountoulakis](http://www1.icsi.berkeley.edu/~kfount/), email: kimon.fountoulakis at uwaterloo dot ca\n- [Meng Liu](https://www.cs.purdue.edu/homes/liu1740/), email: liu1740 at purdue dot edu\n- [David Gleich](https://www.cs.purdue.edu/homes/dgleich/), email: dgleich at purdue dot edu\n- [Michael Mahoney](https://www.stat.berkeley.edu/~mmahoney/), email: mmahoney at stat dot berkeley dot edu\n\n### Contributors\n\n- [Chufeng Hu](https://www.linkedin.com/in/chufeng-hu-545022114/?originalSubdomain=ca), email: chufeng dot hu at uwaterloo dot ca\n- [Yuying Li](https://cs.uwaterloo.ca/~yuying/), email: yuying at uwaterloo dot ca \n- [Ben Johnson](https://github.com/bkj), email: bkj dot 322 at gmail dot com\n\n## List of applications and methods\n\n- [Approximate PageRank](https://dl.acm.org/citation.cfm?id=1170528)\n- [L1-regularized PageRank](https://link.springer.com/article/10.1007/s10107-017-1214-8) (solved using accelerated proximal gradient descent)\n- [PageRank Nibble](https://dl.acm.org/citation.cfm?id=1170528)\n- [Rounding methods for spectral embeddings](https://dl.acm.org/citation.cfm?id=1170528)\n- [MQI](https://link.springer.com/chapter/10.1007/978-3-540-25960-2_25)\n- [FlowImprove](https://dl.acm.org/citation.cfm?id=1347154)\n- [SimpleLocal](https://dl.acm.org/citation.cfm?id=3045595)\n- [Capacity Releasing Diffusion](http://proceedings.mlr.press/v70/wang17b.html)\n- [Multiclass label prediction](https://dl.acm.org/citation.cfm?id=2783376)\n- [Network Community Profiles](http://www.tandfonline.com/doi/abs/10.1080/15427951.2009.10129177)\n- Global spectral partitioning\n- Find k clusters\n- Densest subgraph\n- [Triangle clusters and vertex neighborhood metrics](https://arxiv.org/pdf/1112.0031.pdf)\n- Handy network drawing methods\n\n## Pipelines\n\n- [Network Community Profiles](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/NCPs.ipynb)\n- [Multiclass label prediction](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/multiclasslabelprediction.ipynb)\n- [Triangle clusters and vertex neighborhood metrics](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/TriangleClustersAndVertexNeighborhoodMetrics.ipynb)\n- [Find k clusters](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/find_k_clusters.ipynb)\n\n## Examples\n\nAll examples are in the [notebooks](https://github.com/kfoynt/LocalGraphClustering/tree/master/notebooks) folder.\n\nBelow is a simple demonstration from [test.py](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/test.py) in [notebooks](https://github.com/kfoynt/LocalGraphClustering/tree/master/notebooks) on how to improve spectral partitioning using flow-based methods from local graph clustering.\n\n```python\nfrom localgraphclustering import *\n\nimport time\nimport numpy as np\n\n# Read graph. This also supports gml and graphml format.\ng = GraphLocal('./datasets/senate.edgelist','edgelist',' ')\n\n# Call the global spectral partitioning algorithm.\neig2 = fiedler(g)\n\n# Round the eigenvector\noutput_sc = sweep_cut(g,eig2)\n\n# Extract the partition for g and store it.\neig2_rounded = output_sc[0]\n\n# Conductance before improvement\nprint(\"Conductance before improvement:\",g.compute_conductance(eig2_rounded))\n\n# Start calling SimpleLocal\nstart = time.time()\noutput_SL_fast = SimpleLocal(g,eig2_rounded)\nend = time.time()\nprint(\"running time:\",str(end-start)+\"s\")\n\n# Conductance after improvement\nprint(\"Conductance after improvement:\",g.compute_conductance(output_SL_fast[0]))\n\noutput_SL = output_SL_fast[0]\n```\n\n## Examples with visualization\n\nFor general examples with visualization using our built-in drawing methods, see the Jupyter notebook [examples with visualization](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/new_visualization_examples.ipynb).\n\nFor comparisons of spectral- and flow-based methods with visualization see the Jupyter notebooks [here](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/spectral_vs_flow_with_visualization.ipynb) and [here](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/spectral_vs_flow_2_with_visualization.ipynb).\n\nFor visual demonstration of algorithms that can improve a given seed set of nodes see the Jupyter notebook [here](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/improveType_algorithms_with_visualization.ipynb).\n\n## Scalable graph analytics on your laptop\n\nFor examples using reasonably large graphs (100 million edges) on a 16GB RAM laptop please see the Jupyter notebook [here](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/examples_scalable_graph_analytics.ipynb).\n\n## Advanced examples\n\nFor advanced examples see the Jupyter notebook [here](https://github.com/kfoynt/LocalGraphClustering/blob/master/notebooks/examples.ipynb).\n\n\n## Demonstration: social networks\n\n![](\"images/JHopkins.png\")
![](\"images/Hopkins_global.png\")
\n![](\"images/Hopkins_local_1.png\")
![](\"images/Hopkins_local_2.png\")
\n\n## Demonstration: bioinformatics networks\n\n![](\"images/pss.png\")
![](\"images/pss_local.png\")
\n![](\"images/ppi.png\")
![](\"images/ppi_local.png\")
\n![](\"images/blasted.png\")
![](\"images/blasted_local.png\")
\n\n## Presentation\n\n[](https://youtu.be/mf9h1-jhyos)\n\n## When local graph clustering methods do not perform well?\n\nIn theory and in practice we have observed that the performance of local graph clustering methods\ndepends on the magnitute of the conductance of the target cluster as well as the magnitute of the minimum conductance\nin the induced subgraph of the target cluster. Simply put, if the \"internal connectivity\" of the target cluster (the minimum conductance\nin the induced subgraph of the target cluster) is not stronger than the \"external connectivity\" (the conductance of the target cluster) then local graph clustering methods have poor performance in terms of finding the target cluster. For theoretical details please see Section 3 in the [Capacity Releasing Diffusion for Speed and Locality](http://proceedings.mlr.press/v70/wang17b.html) paper. For extensive numerical experiments that demonstrate properties of challenging target clusters please see Section 4 in [Capacity Releasing Diffusion for Speed and Locality](http://proceedings.mlr.press/v70/wang17b.html) as well as the supplementary material in the same link.\n\n\n## Installation\n\n```\nClone the repo\n```\n```\nEnter the folder using the termimal\n```\n```\nType in the terminal `python setup.py install`\n```\nNote that this package runs only with Python 3.\n\nIt can also be installed through pip:\n\n```\npip3 install localgraphclustering\n```\n\n## Import from Julia\n\n1. In Julia, add the PyCall package: \n\n `Pkg.add(\"PyCall\")`\n2. Update which version of Python that PyCall defaults to:\n\n `ENV[\"PYTHON\"] = (path to python3 executable) `\n\n `Pkg.build(\"PyCall\")`\n\n (You can get the path to the python3 executable by just running \"which python3\" in the terminal.)\n3. Make sure the PyPlot package is added in Julia.\n\n4. Import *localgraphclustering* by using:\n\n `using PyPlot`\n\n `using PyCall`\n\n `@pyimport localgraphclustering`\n\nYou can now use any routine in *localgraphluserting* from Julia.\n\n## License\n\nLocalGraphClustering,\nCopyright (C) 2017, Kimon Fountoulakis, Meng Liu, David Gleich, Michael Mahoney\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\n\n",
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