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Graph wide algorithms#

The following examples cover three graphwide algorithms:

  • Graph Density - which represents the ratio between the edges present in a graph and the maximum number of edges that the graph could contain.
  • Clustering coefficient - which is a measure of the degree to which nodes in a graph tend to cluster together e.g. how many of your friends are also friends.
  • Reciprocity - which is a measure of the likelihood of nodes in a directed network to be mutually connected e.g. if you follow someone on twitter, whats the change of them following you back.

To run an algorithm you simply need to import the algorithm package, choose an algorithm to run, and give it your graph.

from raphtory import algorithms as rp
from raphtory import Graph
import pandas as pd

df = pd.read_csv("../data/lotr.csv")

lotr_graph = Graph()
lotr_graph.load_edges_from_pandas(
    df=df,time="time", src="src", dst="dst"
)

density = rp.directed_graph_density(lotr_graph)
clustering_coefficient = rp.global_clustering_coefficient(lotr_graph)
reciprocity = rp.global_reciprocity(lotr_graph)

print(f"The graph's density is {density}")
print(f"The graph's clustering coefficient is {clustering_coefficient}")
print(f"The graph's reciprocity is {reciprocity}")

Output

The graph's density is 0.03654467730163695
The graph's clustering coefficient is 0.4163023913602468
The graph's reciprocity is 0.19115549215406563