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        "author": "Fuad Aleskerov, Natalia Meshcheryahkova, Sergey Shvydun(*)",
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        "description": "# SLRIC package  <img align=\"middle\" src=\"https://www.hse.ru/data/2019/03/29/1187981536/slric.png\" alt=\"drawing\" width=\"40\"/>\n\n<p>SLRIC is a Python package for influence assessment in networks using short-range interaction centrality (SRIC) and long-range interaction centrality (LRIC) indices.</p>\n<div>\n<p>SRIC and LRIC indices take into account:</p>\n<ul>\n<li>Individual attributes of nodes: threshold of influence (quota), nodes size, etc.;</li>\n<li>Possiblity of the group influence;</li>\n<li>Indirect connections between nodes.</li>\n</ul>\n</div>\n<b>Example:</b>\n<div style=\"display: flex; justify-content: center;\">\n  <img src=\"https://www.hse.ru/data/2019/03/29/1187981382/graph.png\" alt=\"SLRIC features\" width=\"300\"/>\n<ul>\n<li>Nodes 2 and 4 have different thresholds of influence (<i>q</i>);</li>\n<li>Nodes 1 and 2 influence node 4 as a group. Even though node 3 is connected to node 4, it does not influence it directly as node 3 is not a pivotal member in any group;</li>\n<li>Node 3 influences node 4 via node 2.</li>\n</ul>\n<p>Website: <a href=\"https://github.com/SergSHV/slric\" rel=\"nofollow\">https://github.com/SergSHV/slric</a>\n<p>Authors: <a href=\"https://www.hse.ru/en/org/persons/140159\" rel=\"nofollow\">Fuad Aleskerov</a>, <a href=\"https://www.hse.ru/en/staff/natamesc\" rel=\"nofollow\">Natalia Meshcheryakova</a>, <a href=\"https://www.hse.ru/en/staff/Shv\" rel=\"nofollow\">Sergey Shvydun</a> (HSE University, ICS RAS)</div>\n\n## Installation\nInstall the package from PyPI:\n\n    $ pip install slric\n\nYou can also install the latest version from GitHub:\n\n    $ pip install git+https://github.com/SergSHV/slric.git\n\n## Load SLRIC package:\n\n\n    >>> import slric\n\n\n## SRIC/LRIC Calculation for Simple Example\n\nGenerate a network using NetworkX package \n\n\n    >>> import networkx as nx\n    >>> G = nx.DiGraph()\n    >>> G.add_edge(1, 4, weight=7)\n    >>> G.add_edge(2, 4, weight=5)\n    >>> G.add_edge(3, 4, weight=2)\n    >>> G.add_edge(3, 2, weight=6) \n\n\n**Case 1**\n- *q*=60% of weighted in-degree (in percentage);\n-  nodes have the same size (*size* = 1).\n\n[//]: # (Case 1)\n\n\n\n\n    >>> slric.sric(G, q=60, size=1) # SRIC \n    {1: 0.36363636363636365, 4: 0.0, 2: 0.09740259740259741, 3: 0.538961038961039}\n\n    >>> slric.lric(G, q=60, size=1, models='max') # LRIC (Max) \n    {1: 0.27450980392156865, 4: 0.0, 2: 0.1470588235294118, 3: 0.5784313725490197}\n\n    >>> slric.lric(G, q=60, size=1, models='maxmin') # LRIC (MaxMin)\n    {1: 0.27450980392156865, 4: 0.0, 2: 0.1470588235294118, 3: 0.5784313725490197}\n\n    >>> slric.lric(G, q=60, size=1, models='pagerank') # LRIC (PageRank)\n    {1: 0.32165639923246203, 4: 0.0, 2: 0.18808528619697315, 3: 0.49025831457056473}\n\n\n **Case 2** \n- *q*=5 for each node (defined quota, *dq*);\n- nodes have the same size (*size* = 1).\n\n[//]: # (Case 2)\n\n\n    >>> slric.sric(G, dq=5, size=1) # SRIC\n    {1: 0.21153846153846154, 4: 0.0, 2: 0.28846153846153844, 3: 0.5}\n\n    >>> slric.lric(G, dq=5, size=1, models='max') # LRIC (Max)\n    {1: 0.25, 4: 0.0, 2: 0.25, 3: 0.5}\n\n\n**Case 3** \n- *q*=5 for node 2, *q*=10 for node 4;\n- nodes have the same size (*size* = 1).\n\n[//]: # (Case 3)\n\n\n    >>> d = dict()\n    >>> d[2] = 5\n    >>> d[4] = 10\n\n    >>> slric.sric(G, dq=d, size=1) # SRIC\n    {1: 0.2916666666666667, 4: 0.0, 2: 0.20833333333333337, 3: 0.5}\n\n    >>> slric.lric(G, dq=d, size=1, models='max') # LRIC (Max)\n    {1: 0.24137931034482757, 4: 0.0, 2: 0.17241379310344826, 3: 0.5862068965517241}\n\n\n## Write LRIC results to file\n    >>> from slric import lric, GraphQW\n    >>> ranking, lric_graph = lric(G, q=60, size=1, models=['max', 'maxmin'], data=TRUE)\n    >>> GraphQW.write_centrality(lric_graph, 'output.txt', separator=';', mode='w')\n\n\n## Additional features\n1) If nodes size (*size*) is not defined, then *size* = weighted out-degree;\n2) Similarly to threshold of influence (*q*), nodes size can be of dict() type;\n3) Maximal group size can be limited using '*group_size*' parameter (by default, *group_size*=4);\n4) Maximal indirect influence limit can be defined using '*limpath*' parameter (by default, *limpath*=3);\n5) If LRIC version (*models*) is not defined, then LRIC (Max) is calculated by default (*models='max'* ).\n\n## License\n\nBSD 3-Clause License\n\nCopyright (c) 2019. Fuad Aleskerov, Natalia Meshcheryakova, Sergey Shvydun.\n\nAll rights reserved.\n\n\n",
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