{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/FacebookRecruiting/train.csv')\ndata","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.groupby('source_node')['source_node'].value_counts().sort_values()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.isna().any().sum()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data[['source_node', 'destination_node']]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Graphtype = nx.DiGraph()\nG = nx.from_pandas_edgelist(data.head(), source = 'source_node', target = 'destination_node', create_using=Graphtype)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pos=nx.spring_layout(G)\nnx.draw(G,pos,node_color='#87ceeb',edge_color='#fc0362', with_labels=True)\nprint(nx.info(G))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data['source_node'].nunique()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data['source_node'].nunique() / data.shape[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"H = G.to_undirected()\nfor i in nx.ego_graph(H, 1, radius=2, undirected=False).edges:\n    print(nx.jaccard_coefficient(H, i))\n    #list.append(tuple(i))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for u, v, p in nx.jaccard_coefficient(H, nx.ego_graph(H, 1, radius=2, undirected=False).edges):\n    print(p)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}