<jupyter_start><jupyter_text>erdos_renyi_graph :<jupyter_code>#creating the graph with 100 nodes and 0.3 edge probability
er1 = nx.erdos_renyi_graph(100, 0.3)
#plotting erdos_renyi_graph
nx.draw(er1)
#centrality degree of er1
er1_deg_cent = nx.degree_centrality(er1)
#plotting histogram dustribution of er1 graph
plt.figure()
plt.hist(list(er1_deg_cent.values()))
plt.show()<jupyter_output><empty_output><jupyter_text>- As we can see, the histogram distribution for degree of centrality for er1 graph is **Normal Distribution**- For this graph, Mean = Median = Mode<jupyter_code>#creating the graph with 100 nodes and 0.6 edge probability
er2 = nx.erdos_renyi_graph(100, 0.6)
#plotting erdos_renyi_graph2
nx.draw(er2)
#centrality degree of er2
er2_deg_cent = nx.degree_centrality(er2)
#plotting histogram dustribution of er2 graph
plt.figure()
plt.hist(list(er2_deg_cent.values()))
plt.show()<jupyter_output><empty_output><jupyter_text>- The histogram distribution for degree of centrality for er2 graph is **Multimodal Distribution** as it has more than two peaks.- For this graph, Mean = Median = Mode - However, as "edge probability" of graph doubles(i.e from 0.3 to 0.6), the values of "degree of centrality" also doubles.- It can also be said that, degree of centrality is directly propotional to edge probability.  barabasi_albert_graph :<jupyter_code>#creating the graph with 100 nodes and 3 edges to attach
ba = nx.barabasi_albert_graph(100,3)
#degree of centrality of barabasi_albert_graph
ba_deg_cent = nx.degree_centrality(ba)
#plotting histogram distribution
plt.figure()
plt.hist(list(ba_deg_cent.values()))
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09271523178807947, 'fraction_numerical': 0.029500301023479832, 'mean_word_length': 6.554545454545455, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '252133', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 621, 'n_tokens_neox': 567, 'n_words': 172, 'path': '2022-round-1/shivani6320/nx_notebook.ipynb', 'repo_name': 'Lukong123/outreachy', 'size': '1661'}