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python代写-RSEWORK 1
时间：2021-11-04
2021/11/4 03:55Coursework 1
第1/7⻚
COURSEWORK 1 - NETWORK ANALYSIS
In this coursework, you will have to analyse networks based on what you have learned so
far in class. Many exercises will require you to discuss the results of your analysis, some
other will leave you the choice of which algorithm to use for a particular task. This is by
design because this coursework assesses whether you understand network science and
whether you can apply it to real-world networks. For this reason, if you realise you need
to make assumptions to answer a question, do so and always, always motivate your
assumptions and answers!
PART 1 - The Game of Networks
Exercise 1.1 >10 marks@
1.1.1
For each round of the game, compute the following network statistics:
Number of nodes
Number of links
Density
Clustering coefficient
Average degrees (in-degree, out-degree, total degree)
Average path length
Diameter
Size of the giant component
Degree distributions (in-degree, out-degree, total degree)
Centrality distribution (choose one centrality measure, motivating your choice)
In [1]:
import numpy as np
import pandas as pd
import powerlaw as pl
import networkx as nx
import pymfinder as py
2021/11/4 03:55Coursework 1
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1.1.2
Display these results using a pandas dataframe in which rows are the rounds and
columns are the quantities.
1.1.3
In light of these results, provide a brief description of the network (one paragraph
maximum).
1.1.4
What type of network (e.g., random, small world, etc.) do you think this is? Motivate your
answer by using your results.
Exercise 1.2 >10 marks@
Plot the temporal evolution of the quantities you computed for Exercise 1.1 and discuss
their evolution. Specifically, discuss whether the way these quantities evolved is
something you expected, and why.
Note: make sure every plot is clear and it is easy to undertsand which quantity is being
plotted! When discussing the results, be accurate in specifying which quantity/plot you
are referring to.
Exercise 1.3 >10 marks@
1.3.1
Compute 2-node and 3-node motifs for each round (I suggest you use the library
pymfinder, if you decide to use a different one makes sure it works properly). Compute
their significance profile. Report your results in a dataframe.
1.3.2
Only for motifs you think are significant, discuss their meaning and on why you think you
observe them in the network.
1.3.3
Plot the temporal evolution of motifs that you think are significant.
In [ ]:
2021/11/4 03:55Coursework 1
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Exercise 1.4 >10 marks@
1.4.1
Use an algorithm of your choice (motivate your choice by discussing the pros and cons)
to find the communities in the network from the last round. Draw the partitioned network
and print the node memberships.
1.4.2
Discuss your findings from 1.4.1, including any limitation of these results.
PART 2 - Bitcoin Networks
For this part, you will use the bitcoin networks available on Learn under "coursework
data". These networks represent transactions between users. The direction of the links
represents the transaction flow, i.e., a link from i to j represent a transaction from i to j.
Data is provided for the three months between 09-Sep-2013 and 08-Dec-2013, during
which there was a price bubble (from 07-Oct to 23-Nov).
Exercise 2.1 >10 marks@
In [ ]:
In [ ]:
2021/11/4 03:55Coursework 1
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2.1.1
For each weekly network, find the largest weakly and strongly connected components.
For each of them, compute the following network statistics:
Number of nodes
Number of links
Density
Clustering coefficient
Average degrees (in-degree, out-degree, total degree)
Average path length
Diameter
Degree distributions (in-degree, out-degree, total degree)
Centrality distribution (choose one centrality measure, motivating your choice)
2.1.2
Display these results using a pandas dataframe in which rows are the rounds and
columns are the quantities.
2.1.3
In light of these results, provide a brief comparison of the two networks.
2.1.4
What type of network (e.g., random, small world, etc.) do you think these are? Motivate
your answer by using your results.
Exercise 2.2 >15 marks@
In [ ]:
2021/11/4 03:55Coursework 1
第5/7⻚
Plot the temporal evolution of the quantities you computed for Exercise 1.1 and discuss
their evolution. Specifically, include discussion on:
whether the way these quantities evolved is something you expected, and why.
the temporal evoltuion of the computed quantities with respect to the bubble.
similarities and differences of the temporal dynamics of the network quantities
between the two components.
Note: make sure every plot is clear and it is easy to undersand which quantity is being
plotted! When discussing the results, be accurate in specifying which quantity/plot you
are referring to.
Exercise 2.3 >5 marks@
2.3.1
Use an algorithm of your choice (motivate your choice by discussing the pros and cons)
to find the communities in the last weekly network. Draw the corresponding super-
networks of communities.
2.3.2
Discuss your findings from 2.3.1, including any limitation of these results.
Exercise 2.4 >15 marks@
Find the 5 most important nodes in the network. Discuss and motivate your assumptions.
Discuss your results and their implications.
Exercise 2.5 >5 marks@
In [ ]:
In [ ]:
In [ ]:
2021/11/4 03:55Coursework 1
第6/7⻚
Suppose you are working for DogeInvest Ltd, an investment fund specialising in
cryptocurrencies. You convinced them to hire you because you persuaded them that
network science is amazing, and they can make billions by following your advice.
Now, your managers asked you to write a short report (max 3 paragraphs) on the results
of your bitcoin network analysis, and asked you not to use technical terms because they
do not understand network science.
PART 3 - Network Comparison
Exercise 3.1 >10 marks@
Compare the Bitcoin Networks and the Game of Networks networks in light of your
previous results. Include as much detail as necessary to support your analysis and
discussion.
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
2021/11/4 03:55Coursework 1
第7/7⻚
In [ ]:
In [ ]:
学霸联盟
第1/7⻚
COURSEWORK 1 - NETWORK ANALYSIS
In this coursework, you will have to analyse networks based on what you have learned so
far in class. Many exercises will require you to discuss the results of your analysis, some
other will leave you the choice of which algorithm to use for a particular task. This is by
design because this coursework assesses whether you understand network science and
whether you can apply it to real-world networks. For this reason, if you realise you need
to make assumptions to answer a question, do so and always, always motivate your
assumptions and answers!
PART 1 - The Game of Networks
Exercise 1.1 >10 marks@
1.1.1
For each round of the game, compute the following network statistics:
Number of nodes
Number of links
Density
Clustering coefficient
Average degrees (in-degree, out-degree, total degree)
Average path length
Diameter
Size of the giant component
Degree distributions (in-degree, out-degree, total degree)
Centrality distribution (choose one centrality measure, motivating your choice)
In [1]:
import numpy as np
import pandas as pd
import powerlaw as pl
import networkx as nx
import pymfinder as py
2021/11/4 03:55Coursework 1
第2/7⻚
1.1.2
Display these results using a pandas dataframe in which rows are the rounds and
columns are the quantities.
1.1.3
In light of these results, provide a brief description of the network (one paragraph
maximum).
1.1.4
What type of network (e.g., random, small world, etc.) do you think this is? Motivate your
answer by using your results.
Exercise 1.2 >10 marks@
Plot the temporal evolution of the quantities you computed for Exercise 1.1 and discuss
their evolution. Specifically, discuss whether the way these quantities evolved is
something you expected, and why.
Note: make sure every plot is clear and it is easy to undertsand which quantity is being
plotted! When discussing the results, be accurate in specifying which quantity/plot you
are referring to.
Exercise 1.3 >10 marks@
1.3.1
Compute 2-node and 3-node motifs for each round (I suggest you use the library
pymfinder, if you decide to use a different one makes sure it works properly). Compute
their significance profile. Report your results in a dataframe.
1.3.2
Only for motifs you think are significant, discuss their meaning and on why you think you
observe them in the network.
1.3.3
Plot the temporal evolution of motifs that you think are significant.
In [ ]:
2021/11/4 03:55Coursework 1
第3/7⻚
Exercise 1.4 >10 marks@
1.4.1
Use an algorithm of your choice (motivate your choice by discussing the pros and cons)
to find the communities in the network from the last round. Draw the partitioned network
and print the node memberships.
1.4.2
Discuss your findings from 1.4.1, including any limitation of these results.
PART 2 - Bitcoin Networks
For this part, you will use the bitcoin networks available on Learn under "coursework
data". These networks represent transactions between users. The direction of the links
represents the transaction flow, i.e., a link from i to j represent a transaction from i to j.
Data is provided for the three months between 09-Sep-2013 and 08-Dec-2013, during
which there was a price bubble (from 07-Oct to 23-Nov).
Exercise 2.1 >10 marks@
In [ ]:
In [ ]:
2021/11/4 03:55Coursework 1
第4/7⻚
2.1.1
For each weekly network, find the largest weakly and strongly connected components.
For each of them, compute the following network statistics:
Number of nodes
Number of links
Density
Clustering coefficient
Average degrees (in-degree, out-degree, total degree)
Average path length
Diameter
Degree distributions (in-degree, out-degree, total degree)
Centrality distribution (choose one centrality measure, motivating your choice)
2.1.2
Display these results using a pandas dataframe in which rows are the rounds and
columns are the quantities.
2.1.3
In light of these results, provide a brief comparison of the two networks.
2.1.4
What type of network (e.g., random, small world, etc.) do you think these are? Motivate
your answer by using your results.
Exercise 2.2 >15 marks@
In [ ]:
2021/11/4 03:55Coursework 1
第5/7⻚
Plot the temporal evolution of the quantities you computed for Exercise 1.1 and discuss
their evolution. Specifically, include discussion on:
whether the way these quantities evolved is something you expected, and why.
the temporal evoltuion of the computed quantities with respect to the bubble.
similarities and differences of the temporal dynamics of the network quantities
between the two components.
Note: make sure every plot is clear and it is easy to undersand which quantity is being
plotted! When discussing the results, be accurate in specifying which quantity/plot you
are referring to.
Exercise 2.3 >5 marks@
2.3.1
Use an algorithm of your choice (motivate your choice by discussing the pros and cons)
to find the communities in the last weekly network. Draw the corresponding super-
networks of communities.
2.3.2
Discuss your findings from 2.3.1, including any limitation of these results.
Exercise 2.4 >15 marks@
Find the 5 most important nodes in the network. Discuss and motivate your assumptions.
Discuss your results and their implications.
Exercise 2.5 >5 marks@
In [ ]:
In [ ]:
In [ ]:
2021/11/4 03:55Coursework 1
第6/7⻚
Suppose you are working for DogeInvest Ltd, an investment fund specialising in
cryptocurrencies. You convinced them to hire you because you persuaded them that
network science is amazing, and they can make billions by following your advice.
Now, your managers asked you to write a short report (max 3 paragraphs) on the results
of your bitcoin network analysis, and asked you not to use technical terms because they
do not understand network science.
PART 3 - Network Comparison
Exercise 3.1 >10 marks@
Compare the Bitcoin Networks and the Game of Networks networks in light of your
previous results. Include as much detail as necessary to support your analysis and
discussion.
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
2021/11/4 03:55Coursework 1
第7/7⻚
In [ ]:
In [ ]:
学霸联盟
