davisvictorns

# degree sequence
count_sequence = sorted([data['count'] for node, data in list(G.nodes.data())], reverse=True)[1:]

fig, ax = plt.subplots(1,2,figsize=(8,4))

# all_data has information about count_sequence and the width of each bin
ax[0].hist(count_sequence)
ax[1].hist(count_sequence,bins=[x for x in range(80)])

ax[0].set_title("count Histogram")

davisvictorns

plt.style.use("default")
# degree sequence
degree_sequence = sorted([d for n, d in G.degree()], reverse=True)  

fig, ax = plt.subplots(1,2,figsize=(8,4))

# all_data has information about degree_sequence and the width of each bin
ax[0].hist(degree_sequence)
ax[1].hist(degree_sequence,bins=[x for x in range(50)])

davisvictorns

edg_copy = edges.copy()
edge_with_weight = []
while len(edg_copy) > 0:
    curr_edge = edg_copy[0]
    equivalent_edges = []
    # get the equivalent edges
    # ('a', 'b') is equivalent to ('b', 'a') because it's not a directed connection
    for edge in edg_copy:
        is_different = False
        for hashtag in edge:

davisvictorns

edges = []
nodes = []
for i, row in df.iterrows():
    curr_hashtags = row['hashtags'].split(' ')
    nodes += curr_hashtags
    combinations = list(itertools.combinations(curr_hashtags, 2))
    edges += combinations
nodes_unique = set(nodes)

davisvictorns

c = twitter.cursor(twitter.search, q='#7deSetPelaLiberdade', count=100, result_type='mixed', tweet_mode='extended')
tweets = []
count = 0
n_tweets = 30000
for tweet in c:
    count += 1
    tweets.append(tweet)
    # each 100 tweets requires 1 request
    # sleep after 180 requests
    if count > 0 and count % (180 * 100) == 0:

davisvictorns

plt.scatter(degree, clustering)
plt.xlabel("Degree")
plt.ylabel("Clustering")
plt.show()

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degree = [G.degree(node) for node in G.nodes]
clustering = [nx.clustering(G, node) for node in G.nodes]

plt.hist(clustering)
plt.show()

plt.boxplot(clustering)
plt.show()

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acc = nx.average_clustering(nx.Graph(G))
acc

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print("Densidade:", nx.density(G))

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print("Número de nós:", nx.number_of_nodes(G))
print("Número de arestas:", nx.number_of_edges(G))