diff --git a/2022-round-1/NikitaSharma1/nx_notebook.ipynb b/2022-round-1/NikitaSharma1/nx_notebook.ipynb new file mode 100644 index 0000000..bee4fae --- /dev/null +++ b/2022-round-1/NikitaSharma1/nx_notebook.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create an explanatory Jupyter notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#importing libraries\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Erdos renyi graph with 100 nodes and 0.3 probability" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "ERG1= nx.erdos_renyi_graph(100,0.3)\n", + "degree_cent1= nx.degree_centrality(ERG1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plotting histogram of degree centrality of erdos renyi graph\n", + "\n", + "fig1, ax1 = plt.subplots(figsize =(7, 5))\n", + "ax1.hist(list(degree_cent1.values()), color='#96EEE5',edgecolor='#8C9291')\n", + "ax1.set_title(\"Histogram of Degree Centrality Values\", fontsize=15)\n", + "ax1.set_xlabel(\"Degree Centrality\", fontsize=13)\n", + "ax1.set_ylabel(\"Frequency\",fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Erdos renyi graph with 100 nodes and 0.6 probability" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ERG2= nx.erdos_renyi_graph(100,0.6)\n", + "degree_cent2= nx.degree_centrality(ERG2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plotting histogram of degree centrality of erdos renyi graph\n", + "\n", + "fig2, ax2 = plt.subplots(figsize =(7, 5))\n", + "ax2.hist(list(degree_cent2.values()), color='#E8EF7A',edgecolor='#8C9291')\n", + "ax2.set_title(\"Histogram of Degree Centrality Values\", fontsize=15)\n", + "ax2.set_xlabel(\"Degree Centrality\", fontsize=13)\n", + "ax2.set_ylabel(\"Frequency\",fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "erdos_renyi_graph(n,p,seed=None,directed=False): Erdos renyi graph is a random graph which connects each pair of nodes(edge) with probability p. The more the probabilty the more are the chances of having more edges.
\n", + "Degree Centrality: It is the number of edges connected to a node. nx.degree_centrality() returns the centrality after normalising it by dividing the degree of node with the maximun possible degree (n-1).

\n", + "Here, in ERG1, the frequency of nodes having around 0.3 degree centrality is more that means most of the nodes are connected to approx 30 other nodes in this case.
While in ERG2, the frequency of nodes having around 0.6 degree centrality is more that means most of the nodes are connected to approx 60 other nodes in this case.
This difference occured because the probability given in erdos renyi graph function in ERG1 is less than that of in ERG2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barabasi Albert Graph with 100 nodes and 3 edges per new node" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "BAG=nx.barabasi_albert_graph(100,3)\n", + "bag_degree_cent=nx.degree_centrality(BAG)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plotting histogram of degree centrality of Barabasi Albert Graph\n", + "\n", + "fig3, ax3 = plt.subplots(figsize =(7, 5))\n", + "ax3.hist(list(bag_degree_cent.values()), color='#EF817A',edgecolor='#8C9291')\n", + "ax3.set_title(\"Histogram of Degree Centrality Values\", fontsize=15)\n", + "ax3.set_xlabel(\"Degree Centrality\", fontsize=13)\n", + "ax3.set_ylabel(\"Frequency\",fontsize=13)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "barabasi_albert_graph(n, m, seed=None, initial_graph=None): A graph of n nodes is grown by attaching new nodes each with m edges that are preferentially attached to existing nodes with high degree.

\n", + "Here, in BAG, every new node is attaching itself to 3 high degree nodes (m=3) which are mainly the first occuring nodes as you can see in the adjacency list below. The nodes 0,4,9 and 10 have more nodes attached to them than the other nodes. It actually kind of satisfies the logic of the rich getting richer and the poor getting poorer. Hence, the frequency of nodes with higher degree centrality is less as compared to those with lower degree centrality.

\n", + "\n", + "The difference between Erdos Renyi Graph(ERG) and Barabasi Albert Graph(BAG) is that in ERG, each node have equal probaility of connecting while in BAG higher degree nodes have more probability." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1 2 3 4 6 7 10 12 23 28 37 38 45 46 50 57 58 61 63 65 69 80 88 92 99\n", + "1 4 5 7 9\n", + "2 5 6 7 9 10 11 13 14 15 16 17 18 22 25 26 29 32 40 42 51 71 82 83 93 98\n", + "3 4 8 9 11 14 34 35 59 67 96\n", + "4 5 8 12 21 24 27 28 30 45 59 66 70 82 86 95\n", + "5 6 8 12 13 14 16 17 18 25 26 31 33 43 44 54 56 57 58 61 65 77 84 85 90 94\n", + "6 10 11 15 16 42 44 96 99\n", + "7 41 52\n", + "8 35 39 81 87\n", + "9 13 18 20 22 24 29 37 39 41 43 47 49 52 56 62 68 77 78 79 84\n", + "10 21 23 30 32 60 67 69 73 81 94\n", + "11 15 17 24 35 39 43 45 49 51 70 72 80 99\n", + "12 36 68 75 88\n", + "13 52 64\n", + "14 34\n", + "15 19 22 23 28 56 59 75\n", + "16 19 20 21 27 30 42 47 50 53 62 63 64 84\n", + "17 26 27 29 31 36 37 49\n", + "18 19 20 25 40 53 71 75 85 90\n", + "19 36 46 51 55\n", + "20 33 34 38 41 60 70 72 76 86 91\n", + "21 74 89 96\n", + "22 76\n", + "23 69 73 80\n", + "24 32\n", + "25\n", + "26 87\n", + "27 31\n", + "28\n", + "29 38 47 74\n", + "30 88\n", + "31 55 93\n", + "32 33\n", + "33 67 81\n", + "34 60 66 85 91\n", + "35 40 50 58 64 87\n", + "36 46 48 63 92\n", + "37 65\n", + "38 48 62 66\n", + "39\n", + "40\n", + "41 55\n", + "42 61 83\n", + "43 44\n", + "44 48 91\n", + "45 54\n", + "46\n", + "47\n", + "48\n", + "49 53 57 73\n", + "50 54 72\n", + "51\n", + "52\n", + "53\n", + "54 74\n", + "55\n", + "56\n", + "57 98\n", + "58 71 97\n", + "59 76\n", + "60\n", + "61 68 77\n", + "62 83\n", + "63 79\n", + "64 95 97\n", + "65\n", + "66 82 86\n", + "67 79\n", + "68\n", + "69 89 90\n", + "70 78 89\n", + "71 78\n", + "72\n", + "73\n", + "74\n", + "75\n", + "76\n", + "77\n", + "78\n", + "79 94 97\n", + "80\n", + "81 92\n", + "82 98\n", + "83\n", + "84\n", + "85 95\n", + "86\n", + "87\n", + "88\n", + "89 93\n", + "90\n", + "91\n", + "92\n", + "93\n", + "94\n", + "95\n", + "96\n", + "97\n", + "98\n", + "99\n" + ] + } + ], + "source": [ + "for line in nx.generate_adjlist(BAG):\n", + " print(line)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/2022-round-1/NikitaSharma1/nx_pull_requests.txt b/2022-round-1/NikitaSharma1/nx_pull_requests.txt new file mode 100644 index 0000000..7190cbf --- /dev/null +++ b/2022-round-1/NikitaSharma1/nx_pull_requests.txt @@ -0,0 +1 @@ +https://github.com/networkx/networkx/pull/5491 \ No newline at end of file diff --git a/2022-round-1/NikitaSharma1/nx_tutorial_script.py b/2022-round-1/NikitaSharma1/nx_tutorial_script.py new file mode 100644 index 0000000..ea7d9c3 --- /dev/null +++ b/2022-round-1/NikitaSharma1/nx_tutorial_script.py @@ -0,0 +1,39 @@ +''' +----------------------------------- + NX_TUTORIAL_SCRIPT +----------------------------------- +''' + +import networkx as nx + + +''' +Declaration of Directed Graph +''' +G=nx.DiGraph() + +# Adding edges will automatically declare the nodes + +G.add_edge(5,6) +G.add_edge(1,(10,11)) +G.add_edge("spam",5) +G.add_edge((10,11),1) +G.add_edge("test",6) +G.add_edge(5,1) +G.add_edge(1.5,"test") +G.add_edge("spam",(10,11)) + + +''' +Finding shortest path +''' +Shortest_Path= nx.shortest_path(G) +print(Shortest_Path) + +''' +Plotting the directed graph +''' +layout=nx.planar_layout(G) + +nx.draw(G,pos=layout,arrows=True,with_labels=True,node_size=2600,node_color='#4ABF8C', + node_shape='8',width=1.5) \ No newline at end of file