中心性算法

# Install necessary dependencies
%pip install graphdatascience pandas

from graphdatascience import GraphDataScience
import pandas as pd
import os

NEO4J_URI = os.environ.get("NEO4J_URI", "bolt://#:7687")
NEO4J_AUTH = None
if os.environ.get("NEO4J_USER") and os.environ.get("NEO4J_PASSWORD"):
    NEO4J_AUTH = (
        os.environ.get("NEO4J_USER"),
        os.environ.get("NEO4J_PASSWORD"),
    )

gds = GraphDataScience(NEO4J_URI, auth=NEO4J_AUTH)

from graphdatascience.server_version.server_version import ServerVersion

assert gds.server_version() >= ServerVersion(1, 8, 0)

nodes_info_df = pd.read_csv("https://snap.stanford.edu/data/reachability-meta.csv.gz", compression="gzip")
nodes_info_df.head()

routes_df = pd.read_csv(
    "https://snap.stanford.edu/data/reachability.txt.gz",
    sep=" ",
    skiprows=6,
    header=None,
    compression="gzip",
    names=["Origin", "Destination", "Weight"],
)
routes_df.head()

gds.run_cypher(
    "UNWIND $nodes AS node CREATE (n:City {node_id: node.node_id, name: node.name, population: node.metro_pop})",
    params={"nodes": nodes_info_df.to_dict("records")},
)

gds.run_cypher(
    """
    UNWIND $rels AS rel
    MATCH (source:City {node_id: rel.Origin}), (target:City {node_id: rel.Destination})
    CREATE (source)-[:HAS_FLIGHT_TO]->(target)
    """,
    params={"rels": routes_df.to_dict("records")},
)

G, result = gds.graph.project("airline", "City", "HAS_FLIGHT_TO")

print(f"The projection took {result['projectMillis']} ms")

# We can use convenience methods on `G` to check if the projection looks correct
print(f"Graph '{G.name()}' node count: {G.node_count()}")
print(f"Graph '{G.name()}' node labels: {G.node_labels()}")
print(f"Graph '{G.name()}' relationship count: {G.relationship_count()}")

eigenvector_centrality_result = gds.eigenvector.mutate(G, maxIterations=100, mutateProperty="eigenvectorCentrality")

# We can verify that the eigenvectorCentrality was mutated
G.node_properties()

if eigenvector_centrality_result.didConverge:
    print(
        f"The number of iterations taken by Eigenvector Centrality to run is {eigenvector_centrality_result.ranIterations}."
    )
else:
    print("Algorithm did not converge!")

eigenvector_centrality_result.centralityDistribution

gds.graph.nodeProperties.write(G, ["eigenvectorCentrality"])

def display_top_20_cities(centrality_measure):
    """
    Function to execute the Cypher query to retrieve the top 20 cities with the highest centrality measure.
    """
    query = f"""
    MATCH (n:City)
    RETURN n.node_id AS node_id, n.name AS name, n.population AS population, n.{centrality_measure} AS {centrality_measure}
    ORDER BY n.{centrality_measure} DESC
    LIMIT 20
    """
    result = gds.run_cypher(query)

    # Display the result
    print(result)


display_top_20_cities("eigenvectorCentrality")

betweenness_centrality_result = gds.betweenness.mutate(G, mutateProperty="betweennessCentrality")

# We can verify that the betweennessCentrality was mutated
G.node_properties()

betweenness_centrality_result.centralityDistribution

gds.graph.nodeProperties.write(G, ["betweennessCentrality"])

display_top_20_cities("betweennessCentrality")

degree_centrality_result = gds.degree.mutate(G, mutateProperty="degreeCentrality")

# We can verify that the degreeCentrality was mutated
G.node_properties()

degree_centrality_result.centralityDistribution

gds.graph.nodeProperties.write(G, ["degreeCentrality"])

display_top_20_cities("degreeCentrality")

# Cleanup GDS
G.drop()

# Cleanup database
gds.run_cypher("MATCH (n:City) DETACH DELETE n")