First we create a network linking cities with their distance.<\/p>\n\n\n\n
edgelist = [['Mannheim', 'Frankfurt', 85], We create a graph with the previous data.<\/p>\n\n\n\n
g = nx.Graph()Question 1: Find out distinct continents and their cities from this graph.<\/strong><\/p>\n\n\n\nfor i, x in enumerate(nx.connected_components(g)): print(\"cc\"+str(i)+\":\",x)<\/pre>\n\n\n\nQuestion 2: Find the shortest path between Stuttgart and Frankfurt and its length.<\/strong><\/p>\n\n\n\nprint(nx.shortest_path(g, 'Stuttgart','Frankfurt',weight='weight')) Question 3: Find the shortest path among the cities of the graph.<\/strong><\/p>\n\n\n\nfor x in nx.all_pairs_dijkstra_path(g,weight='weight'): print(x)<\/pre>\n\n\n\nMinimum spannig tree<\/h4>\n\n\n\n Question 4: We need to connect all the cities in the graph we have using the minimum amount of wire\/pipe. How do we do this?<\/strong><\/p>\n\n\n\n#nx.minimum_spanning_tree(g) returns a instance of type nx.minimum_spanning_tree(g) returns a instance of type graph<\/pre>\n\n\n\ngraphnx.draw_networkx(nx.minimum_spanning_tree(g)) <\/pre>\n\n\n\nPAGE RANK<\/h4>\n\n\n\n It assigns scores to pages based on the number and quality of incoming and outgoing links. Pagerank can be used anywhere where we want to estimate node importance in any network.<\/p>\n\n\n\n
Create Link between user if user A follows user B and Link between user and Tweets if user tweets\/retweets a tweet.<\/p>\n\n\n\n
For this we are finally going to retrieve the Facebook Network data collection (!)<\/p>\n\n\n\n
fb = nx.read_edgelist('\/kaggle\/input\/facebook-social-network\/facebook-combined.txt', create_using = nx.Graph(), nodetype = int)<\/pre>\n\n\n\n.. and let us plot the network to see how does it look like:<\/p>\n\n\n\n
pos = nx.spring_layout(fb)<\/pre>\n\n\n\nimport warnings \nwarnings.filterwarnings('ignore')\nwarnings.simplefilter('ignore')\nimport matplotlib.pyplot as plt\n\nplt.style.use('fivethirtyeight') \nplt.rcParams['figure.figsize'] = (20, 15)\nplt.axis('off')\n\nnx.draw_networkx(fb, pos, with_labels = False, node_size = 35) \nplt.show()<\/pre>\n\n\n\nLet us compute the page rank for all the nodes.<\/p>\n\n\n\n
pageranks = nx.pagerank(fb)Let us now order the page rank results<\/p>\n\n\n\n
import operatorQuestion 5: Create a subgraph with more influential users. <\/strong><\/p>\n\n\n\nfirst_degree_connected_nodes = list(fb.neighbors(3437))
for x in first_degree_connected_nodes:\n\n second_degree_connected_nodes+=list(fb.neighbors(x)) \n\nsecond_degree_connected_nodes.remove(3437) \nsecond_degree_connected_nodes = list(set(second_degree_connected_nodes))\n\nsubgraph_3437 = nx.subgraph(fb, first_degree_connected_nodes + second_degree_connected_nodes)\n\npos = nx.spring_layout(subgraph_3437)<\/pre>\n\n\n\nAnd we visualise the most influential users painting them in yellow : <\/p>\n\n\n\n
import matplotlib.pyplot as plt\n\nnode_color = ['yellow' if v == 3437 else 'red' for v in subgraph_3437]\n\nnode_size = [1000 if v == 3437 else 35 for v in subgraph_3437] \n\nplt.style.use('fivethirtyeight') \n\nplt.rcParams['figure.figsize'] = (20, 15)\n\nplt.axis('off')\n\nnx.draw_networkx(subgraph_3437, pos, with_labels = False, node_color=node_color,node_size=node_size )\n\nplt.show()<\/pre>\n\n\n\nCentrality measures<\/h4>\n\n\n\n Betweenness centrality quanties how many times a particular node
Degree Centrality:<\/em><\/strong> It is simply the number of connections for a node.<\/p>\n\n\n\nHere is the code for finding the Betweenness centrality for the subgraph.<\/p>\n\n\n\n
pos = nx.spring_layout(subgraph_3437) \n\nbetweennessCentrality = nx.betweenness_centrality(subgraph_3437,normalized=True, endpoints=True)\n\nnode_size = [v * 10000 for v in betweennessCentrality.values()] \n\nplt.figure(figsize=(20,20))\n\nnx.draw_networkx(subgraph_3437, pos=pos, with_labels=False, node_size=node_size )\n\nplt.axis('off') <\/pre>\n\n\n\nThis notebook has been proposed by https:\/\/towardsdatascience.com\/data-scientists-the-five-graph-algorithms-that-you-should-know-30f454fa5513<\/p>\n","protected":false},"excerpt":{"rendered":"
Objective Learn how to explore networks represented by graphs using 5 well known graph operations that you should know. Therefore, the exercise uses a facebook network data set. Material Kaggle account Python notebook Facebook_Social_Network dataset available in Kaggle: https:\/\/www.kaggle.com\/roshansharma\/facebook-social-network ToDo 1 Upload the data set searching on the Kaggle Datasets Explorer and add it to […]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"class_list":["post-310","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/310","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/comments?post=310"}],"version-history":[{"count":15,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/310\/revisions"}],"predecessor-version":[{"id":447,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/310\/revisions\/447"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/media?parent=310"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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