Visualize High-Dimensional Network Data with 3D Scatter Plot

3DScatterGraph-1.py

#!pip install -U node2vec

# load packages
import pandas as pd
import networkx as nx
from node2vec import Node2Vec
from matplotlib import pyplot as plt, rc, cm
from matplotlib.pyplot import figure
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
from sklearn.manifold import TSNE


# read data from github
edge = pd.read_csv("https://raw.githubusercontent.com/jinhangjiang/Datasets/main/Network%20Data/emailEUcore/email-Eu-core.txt",delimiter = " ",names=["Source","Target"])
edge["weight"] = 1 * len(edge)
label = pd.read_csv("https://raw.githubusercontent.com/jinhangjiang/Datasets/main/Network%20Data/emailEUcore/email-Eu-core-department-labels.txt",delimiter = " ",names=["Vertex","Label"])
label.Vertex = label.Vertex.astype(str)


# generate graphs
def convert_graph(edgelist,name):
  graph=nx.Graph()
  graph.add_weighted_edges_from([tuple(x) for x in edgelist.values])
  graph.name = "Covid DisNet for" + " " + name
  print(nx.info(graph))
  print("------------------------------------")
  print("************************************")
  print("------------------------------------")
  return graph
  
graph = convert_graph(edge, "eu email")


# calculate a starting point for vector_size for node2vec
vector_size = round(len(graph.nodes)**0.25)


# use node2vec to get embeddings
setup = Node2Vec(graph,dimensions=vector_size, walk_length=5, num_walks=5)
model = setup.fit(window=10, min_count=1)
print("--------------------------------------")