- 5th Jul 2024
- 12:36 pm

In this assignment our experts can provide valuable assistance by offering clear explanations of complex concepts, detailed guidance on methodologies, and effective strategies for presenting results. Whether it's interpreting theoretical frameworks, implementing formulas, or structuring findings, they ensure clarity and precision in your work.The content may include the following key points:

- In each task, briefly tell us how you calculate them, theory, formula, implementation, or other details you think necessary,
- then report your answer directly in tables, lists, figures or other kinds of forms that can clearly deliver your results.
- In the end, illustrate your opinions, analysis or findings.
- Maximum one page for each task (excluding the results)

**Data Analysis and Reporting Framework for Task Results - Get Assignment Solution**

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**Free Assignment Solution - Data Analysis and Reporting Framework for Task Results **

#

"""

import time

import networkx as nx

from collections import deque

from collections import OrderedDict

import matplotlib.pyplot as plt

DATASET = 'facebook.txt'

bcen = []

prn = []

def load_edges(filepath):

G = nx.to_undirected(nx.read_edgelist(path=filepath, nodetype=int))

return G, G.nodes(), G.adj

def normalize(n, value):

return value/((n - 1) * (n - 2))

def get_top_n_nodes(nodes, n=10, nx=False):

top = OrderedDict(sorted(nodes.items(), key=lambda kv: kv[1], reverse=True)[:n])

for node, cb in top.items():

if nx:

bcen.append(node)

else:

bcen.append(node)

return [n for n in top.keys()]

def load_edges_pr(filepath, type=int):

G = nx.read_edgelist(path=filepath, nodetype=type)

G = G.to_undirected()

return G

def get_top_n_nodes_pr(nodes, n=10, nx=False):

top = OrderedDict(sorted(nodes.items(), key=lambda kv: kv[1], reverse=True)[:n])

for node, pagerank in top.items():

if nx:

prn.append(node)

else:

prn.append(node)

return [n for n in top.keys()]

def page_rank(G, alpha=0.85, epsilon=1e-4, max_iter=100):

N = G.number_of_nodes()

A = G.adj

D = dict((n, 1/d) for n, d in G.degree())

c = dict((n, 1.0) for n in G)

for _ in range(max_iter):

prev_c = c

c = dict((n, 0.0) for n in G)

for node in A:

for nbr in A[node]:

c[nbr] += alpha * D[node] * prev_c[node]

c[node] += (1 - alpha)

delta = (np.linalg.norm([c[n] - prev_c[n] for n in c]))

if delta < N * epsilon:

return c

def main():

G, vertices, neighbors = load_edges(DATASET)

get_top_n_nodes(nx.betweenness_centrality(G), nx=True)

###################

get_top_n_nodes_pr(nx.pagerank(load_edges_pr(DATASET)), nx=True)

b = [str(k) for k in bcen]

c = [str(k) for k in prn]

with open("out.txt", "w") as outfile:

outfile.write("\t".join(b))

outfile.write("\n")

outfile.write("\t".join(c))

main()

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**About The Author - John Smith**

John Smith is a seasoned data scientist with a passion for exploring complex datasets and deriving meaningful insights. With over a decade of experience in statistical analysis and machine learning, John has contributed to various research projects and industry applications. His expertise includes data visualization, predictive modeling, and algorithm development.