Networkx common operations summary (for personal use)

Catalog

1. Create diagrams

2. Calculate the topological characteristics of the graph

3. Number of network nodes 、 The number of sides

4. Neighbors of a node

5. Visualization network

6. Network longest path

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1. Create diagrams

import networkx as nx

#  Undirected graph 
G = nx.Graph()

#  Directed graph 
G = nx.DiGraph()

#  Add edge ,temp yes DataFrame
for i in tqdm(range(len(temp))):
    G.add_edge(temp['prevshareuuid'][i], temp['shareuuid'][i])

#  Add edge ,data yes dict
G.add_edges_from(data.items())

#  Generate an undirected graph according to the adjacency matrix 
G = nx.from_numpy_matrix(np.array(data))

#  Generate a directed graph according to the adjacency matrix 
G = nx.from_numpy_matrix(np.array(data), create_using=nx.DiGraph)

#  Generate a directed graph according to the edge list 
G = nx.from_pandas_edgelist(data[['source', 'target']], source='source', target='target', create_using=nx.DiGraph)

2. Calculate the topological characteristics of the graph

#  Betweenness 
nx.betweenness_centrality(G)

#  Eigenvector Center 
nx.eigenvector_centrality_numpy(G)

# pagerank
nx.pagerank(G)

3. Number of network nodes 、 The number of sides

G.number_of_nodes()
G.number_of_edges()

4. Neighbors of a node

#  All that is returned is dict
#  Undirected graph 
G[node]

#  Directed graph 
G.degree()
G.out_degree()
G.in_degree()

5. Visualization network

nx.draw_networkx(G)

6. Network longest path

#  Return longest path , One node Of list
nx.dag_longest_path(G)