辅导CMT224程序、辅导Python编程
- 首页 >> Database作业 Module Code: CMT224
Module Title: Social Computing
Lecturer: Dr Liam Turner
Assessment Title: Social Computing Portfolio
Assessment Number: 1
Date Set: 18th July 2022
Submission Date and Time: 8th August 2022 at 9:30am
Return Date: 5th September 2022
This assignment is worth 100% of the total marks available for this module. If coursework is
submitted late (and where there are no extenuating circumstances):
1 If the assessment is submitted no later than 24 hours after the deadline,
the mark for the assessment will be capped at the minimum pass mark;
2 If the assessment is submitted more than 24 hours after the deadline, a
mark of 0 will be given for the assessment.
Your submission must include the official Coursework Submission Cover sheet, which can be
found here:
https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf
Submission Instructions
Description Type Name
Cover sheet Compulsory One PDF (.pdf) file [student_number].pdf
Part 1 Notebook
(Using the template provided on Learning
Central)
Compulsory One IPython Notebook file (.ipynb) [student_number]-part-1.ipynb
Part 2 Notebook
(Using the template provided on Learning
Central)
Compulsory One IPython Notebook file (.ipynb) [student_number]-part-2.ipynb
Part 3 Notebook
(Using the template provided on Learning
Central)
Compulsory One IPython Notebook file (.ipynb) [student_number]-part-3.ipynb
Any code submitted will be run on a system equivalent to your University provided laptop
and must be submitted as stipulated in the instructions above.
Any deviation from the submission instructions above (including the number and types of
files submitted) will result in a mark of zero for the assessment or question part.
Staff reserve the right to invite students to a meeting to discuss coursework submissions
Assignment
You are tasked with analysing various datasets representing different types of social and
communication behaviour. These datasets are provided as files and can be found alongside
this coursework pro-forma on Learning Central. You should ONLY use the files provided as
they are intentionally modified subsets of public datasets1.
Alongside the dataset files, there are 3 (THREE) IPython notebooks, named part-1.ipynb,
part-2.ipynb, and part-3.ipynb, which you should solely use to complete the assignment and
submit these in line with the Submission Instructions section above. The cells in each
completed notebook will be run in the order that they appear. You do not need to resubmit
the dataset files.
You are required to address 16 total questions across the 3 parts. Each part is made up of 1
or 2 tasks containing multiple questions. These questions are also listed below for
convenience.
For EACH question in EACH notebook:
1. Complete the cell below each question marked with “#CODE:” with the Python code
needed to generate any new information you need for your answer. This information
should be outputted when the cell is ran and any floating-point values should be
presented to 2 decimal places unless they are less than 0.01.
2. Complete the cell below this marked with “ANSWER:” with your answer to the
question, referring to the information outputted above (as well as any previous cell if
needed). In doing so, briefly explain your approach and methods/measures used to
answer the question and justify any choices made. Each answer cell should (ideally)
be no more than 125 words.
Each question is worth 6 marks (making a total of 96/100 possible marks) and a further 4
marks (4/100) will awarded for the overall usability and readability of the notebooks
submitted. Marks will be awarded using the criteria described in the Criteria for assessment
section below.
You may use any Python packages locally installed or installable via pip on your University
provided laptop. “%pip install ” commands should be placed in the cell
below “Install Python packages (pip only)” provided at the top of each notebook. “import
” lines for all packages required for the notebook to be ran successfully
should be placed in the cell under “Import Python packages” provided at the top of each
notebook. You may add additional cells throughout the notebooks, but this should be
minimised.
1 Jure Leskovec, & Andrej Krevl. SNAP Datasets: Stanford Large Network Dataset Collection.
http://snap.stanford.edu/data
Questions (Duplicated from the notebook files)
Part 1: Social media behaviour data
Task 1 of 1
Examine the Graph Modelling Language (gml) files
"socialmedia_cmt224r_reply_network.gml" (reply network) and
"socialmedia_cmt224r_social_network.gml" (social network) which represent Twitter data
between a sample of users over several days at the time of the Higgs boson particle discovery.
Both networks are directed and share the same ids for nodes (anonymised Twitter users).
However, the shared user ids are contained within the "label" attribute in the .gml files, not
the node "id" attribute of each individual .gml file.
In the reply network, an edge from a node, , to some other node, , indicates that replied
to a Tweet made by during the time period. Replies are also Tweets. Edges are weighted
with the weight representing the number of times this happened over the time period.
In the social network, an edge from node to indicates that follows on the social media
platform.
Using these networks, answer the following questions:
Q1. How does the topological structure of the reply network differ from the social network
in terms of the fraction of mutual connections (i.e., users that follow each other) and
the number of connected groups of users?
Q2. Do the 20 users that follow the most other users also reply to the most amount of
users?
Q3. To what extent does the number of followers a user has in the social network correlate
with the number of users that have replied to them?
Q4. Do users typically ONLY reply to Tweets, are ONLY replied to, or BOTH?
Q5. Of the users that ONLY reply to Tweets, how many ONLY do so to those users they are
following?
Q6. How many users have ONLY mutual following connections AND ONLY mutual reply
connections with these SAME users?
Part 2: Email behaviour data
Task 1 of 2
Examine the file "emails_cmt224r.edgelist" which represents email behaviour at an
organisation. Each line contains two numbers, and , separated by a blank space. Consider
each number as an identifier for an individual in an organisation, with the space on each line
representing that the individual, , sent at least one email to another individual, , at some
point. Model the data using an appropriate network representation and answer the following
questions:
Q1. How many individuals have a higher or lower ratio of mutual connections than the
ratio of mutual connections found in the overall network?
Q2. Are occurrences of induced, connected subgraphs of 3 individuals (triads) with only
mutual connections more abundant in the network than those with a mixture of
asymmetric and mutual edges?
Q3. Using the largest, strongly connected component (where at least one path exists
between each individual and all others), could the connectivity be suggested to be
reflective of a small world phenomenon in comparison to a comparative random
network?
Task 2 of 2
Examine the JSON file "emails_cmt224_departments.json" (departments file). Keys in the
departments file represent individuals using the same ids as in the
"emails_cmt224r.edgelist" file in Part 2, Task 1 and the values represent a department id
that the individual can be attributed to. Using the contents of the departments file in
combination with the network in Part 2, Task 1, answer the following questions:
Q1. Using the connections that individuals have in the network, are they more likely to
mix with others in their department or those with a similar number of connections?
Q2. Are all departments with 12 or more members more tightly connected amongst
themselves in comparison to all individuals across the overall network irrespective of
their department? Where in this context, 'more tightly connected' is defined as
having less sparsity in the connections among members AND more clustered
connections. In addition to answering the overall question as yes or no, provide a list
of departments this is true for (if any) and not true for (if any).
Part 3: Peer-to-peer message behaviour data
Task 1 of 2
Examine the file "p2p_msg_cmt224r.csv" which represents messaging behaviour between
users on a messaging platform. Each row has four columns, representing a single event where
a person (person_a) messaged another person (person_b) on some date (date) at some time
of day (time). From this, answer the following questions:
Q1. Select a suitable network structure and build a network to represent social
connections based on the messaging behaviour that took place in the first 7 days. In
doing so, assume that one or more messages from one person to another represents
a MUTUAL underlying social connection (i.e., regardless of whether person_a
messaged person_b, vice versa, or both).
Q2. Build another suitable network to represent social connections based on ALL
message behaviour in the dataset. In doing so, assume that one or messages from
one person to another represents a MUTUAL underlying social connection (i.e.,
regardless of whether person_a messaged person_b, vice versa, or both). Can the
social phenomenon, ‘Triadic Closure’, be supported for the COMMON nodes that
exist in both the network created from data from the first 7 days (i.e., from Task 1,
Q1) and the network built from all message behaviour.
Q3. Using the largest connected component of the cumulative network constructed in
Task 1, Q2, what is the average and standard deviation of the MAXIMUM degree of
separation between an individual and all others?
Task 2 of 2
Using the largest connected component of the social network constructed from all data in
Task 1, Q2, assume the role of an outsider with complete visibility of the network that now
wishes to spread a hypothetical message such that everyone in the component would know
the information it contained as quickly as possible. Assume that messages will now spread in
sequential timesteps using the following mechanism. If an individual is told the message at
timestep , the individual will forward the message to all of their direct connections at
timestep +1. Individuals can therefore be told the message more than once. From this,
answer the following questions using network analysis measures as part of the approach:
Q1. Assume that you have to choose 1 individual to tell the message to at timestep 0.
What set of individuals could you choose this individual from and how many
timesteps would be needed for everyone in the component to receive the message?
Q2. Assume that you have to choose 5 individuals to tell the message to at timestep 0.
Provide an example set of 5 individuals that would result in the message being
received by everyone in the component in fewer timesteps than in Q1.
Learning Outcomes Assessed
1. Analyse fundamental traits of complex networks by synthesising theoretical concepts
and methodologies from graph theory.
2. Evaluate and implement computational approaches to model and visualise complex
social phenomena.
3. Design and create software to investigate or support human interaction behaviour.
Criteria for assessment
Credit will be awarded against the following criteria. There are 100 marks available for this
assignment. Each of the 16 questions are worth 6 marks, split between up to 3 marks for the
approach and implementation and up to 3 marks for the explanations and justifications of
the approach and implementation. This totals 96/100 possible marks. Marks will be
awarded using the following criteria:
Module Title: Social Computing
Lecturer: Dr Liam Turner
Assessment Title: Social Computing Portfolio
Assessment Number: 1
Date Set: 18th July 2022
Submission Date and Time: 8th August 2022 at 9:30am
Return Date: 5th September 2022
This assignment is worth 100% of the total marks available for this module. If coursework is
submitted late (and where there are no extenuating circumstances):
1 If the assessment is submitted no later than 24 hours after the deadline,
the mark for the assessment will be capped at the minimum pass mark;
2 If the assessment is submitted more than 24 hours after the deadline, a
mark of 0 will be given for the assessment.
Your submission must include the official Coursework Submission Cover sheet, which can be
found here:
https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf
Submission Instructions
Description Type Name
Cover sheet Compulsory One PDF (.pdf) file [student_number].pdf
Part 1 Notebook
(Using the template provided on Learning
Central)
Compulsory One IPython Notebook file (.ipynb) [student_number]-part-1.ipynb
Part 2 Notebook
(Using the template provided on Learning
Central)
Compulsory One IPython Notebook file (.ipynb) [student_number]-part-2.ipynb
Part 3 Notebook
(Using the template provided on Learning
Central)
Compulsory One IPython Notebook file (.ipynb) [student_number]-part-3.ipynb
Any code submitted will be run on a system equivalent to your University provided laptop
and must be submitted as stipulated in the instructions above.
Any deviation from the submission instructions above (including the number and types of
files submitted) will result in a mark of zero for the assessment or question part.
Staff reserve the right to invite students to a meeting to discuss coursework submissions
Assignment
You are tasked with analysing various datasets representing different types of social and
communication behaviour. These datasets are provided as files and can be found alongside
this coursework pro-forma on Learning Central. You should ONLY use the files provided as
they are intentionally modified subsets of public datasets1.
Alongside the dataset files, there are 3 (THREE) IPython notebooks, named part-1.ipynb,
part-2.ipynb, and part-3.ipynb, which you should solely use to complete the assignment and
submit these in line with the Submission Instructions section above. The cells in each
completed notebook will be run in the order that they appear. You do not need to resubmit
the dataset files.
You are required to address 16 total questions across the 3 parts. Each part is made up of 1
or 2 tasks containing multiple questions. These questions are also listed below for
convenience.
For EACH question in EACH notebook:
1. Complete the cell below each question marked with “#CODE:” with the Python code
needed to generate any new information you need for your answer. This information
should be outputted when the cell is ran and any floating-point values should be
presented to 2 decimal places unless they are less than 0.01.
2. Complete the cell below this marked with “ANSWER:” with your answer to the
question, referring to the information outputted above (as well as any previous cell if
needed). In doing so, briefly explain your approach and methods/measures used to
answer the question and justify any choices made. Each answer cell should (ideally)
be no more than 125 words.
Each question is worth 6 marks (making a total of 96/100 possible marks) and a further 4
marks (4/100) will awarded for the overall usability and readability of the notebooks
submitted. Marks will be awarded using the criteria described in the Criteria for assessment
section below.
You may use any Python packages locally installed or installable via pip on your University
provided laptop. “%pip install ” commands should be placed in the cell
below “Install Python packages (pip only)” provided at the top of each notebook. “import
” lines for all packages required for the notebook to be ran successfully
should be placed in the cell under “Import Python packages” provided at the top of each
notebook. You may add additional cells throughout the notebooks, but this should be
minimised.
1 Jure Leskovec, & Andrej Krevl. SNAP Datasets: Stanford Large Network Dataset Collection.
http://snap.stanford.edu/data
Questions (Duplicated from the notebook files)
Part 1: Social media behaviour data
Task 1 of 1
Examine the Graph Modelling Language (gml) files
"socialmedia_cmt224r_reply_network.gml" (reply network) and
"socialmedia_cmt224r_social_network.gml" (social network) which represent Twitter data
between a sample of users over several days at the time of the Higgs boson particle discovery.
Both networks are directed and share the same ids for nodes (anonymised Twitter users).
However, the shared user ids are contained within the "label" attribute in the .gml files, not
the node "id" attribute of each individual .gml file.
In the reply network, an edge from a node, , to some other node, , indicates that replied
to a Tweet made by during the time period. Replies are also Tweets. Edges are weighted
with the weight representing the number of times this happened over the time period.
In the social network, an edge from node to indicates that follows on the social media
platform.
Using these networks, answer the following questions:
Q1. How does the topological structure of the reply network differ from the social network
in terms of the fraction of mutual connections (i.e., users that follow each other) and
the number of connected groups of users?
Q2. Do the 20 users that follow the most other users also reply to the most amount of
users?
Q3. To what extent does the number of followers a user has in the social network correlate
with the number of users that have replied to them?
Q4. Do users typically ONLY reply to Tweets, are ONLY replied to, or BOTH?
Q5. Of the users that ONLY reply to Tweets, how many ONLY do so to those users they are
following?
Q6. How many users have ONLY mutual following connections AND ONLY mutual reply
connections with these SAME users?
Part 2: Email behaviour data
Task 1 of 2
Examine the file "emails_cmt224r.edgelist" which represents email behaviour at an
organisation. Each line contains two numbers, and , separated by a blank space. Consider
each number as an identifier for an individual in an organisation, with the space on each line
representing that the individual, , sent at least one email to another individual, , at some
point. Model the data using an appropriate network representation and answer the following
questions:
Q1. How many individuals have a higher or lower ratio of mutual connections than the
ratio of mutual connections found in the overall network?
Q2. Are occurrences of induced, connected subgraphs of 3 individuals (triads) with only
mutual connections more abundant in the network than those with a mixture of
asymmetric and mutual edges?
Q3. Using the largest, strongly connected component (where at least one path exists
between each individual and all others), could the connectivity be suggested to be
reflective of a small world phenomenon in comparison to a comparative random
network?
Task 2 of 2
Examine the JSON file "emails_cmt224_departments.json" (departments file). Keys in the
departments file represent individuals using the same ids as in the
"emails_cmt224r.edgelist" file in Part 2, Task 1 and the values represent a department id
that the individual can be attributed to. Using the contents of the departments file in
combination with the network in Part 2, Task 1, answer the following questions:
Q1. Using the connections that individuals have in the network, are they more likely to
mix with others in their department or those with a similar number of connections?
Q2. Are all departments with 12 or more members more tightly connected amongst
themselves in comparison to all individuals across the overall network irrespective of
their department? Where in this context, 'more tightly connected' is defined as
having less sparsity in the connections among members AND more clustered
connections. In addition to answering the overall question as yes or no, provide a list
of departments this is true for (if any) and not true for (if any).
Part 3: Peer-to-peer message behaviour data
Task 1 of 2
Examine the file "p2p_msg_cmt224r.csv" which represents messaging behaviour between
users on a messaging platform. Each row has four columns, representing a single event where
a person (person_a) messaged another person (person_b) on some date (date) at some time
of day (time). From this, answer the following questions:
Q1. Select a suitable network structure and build a network to represent social
connections based on the messaging behaviour that took place in the first 7 days. In
doing so, assume that one or more messages from one person to another represents
a MUTUAL underlying social connection (i.e., regardless of whether person_a
messaged person_b, vice versa, or both).
Q2. Build another suitable network to represent social connections based on ALL
message behaviour in the dataset. In doing so, assume that one or messages from
one person to another represents a MUTUAL underlying social connection (i.e.,
regardless of whether person_a messaged person_b, vice versa, or both). Can the
social phenomenon, ‘Triadic Closure’, be supported for the COMMON nodes that
exist in both the network created from data from the first 7 days (i.e., from Task 1,
Q1) and the network built from all message behaviour.
Q3. Using the largest connected component of the cumulative network constructed in
Task 1, Q2, what is the average and standard deviation of the MAXIMUM degree of
separation between an individual and all others?
Task 2 of 2
Using the largest connected component of the social network constructed from all data in
Task 1, Q2, assume the role of an outsider with complete visibility of the network that now
wishes to spread a hypothetical message such that everyone in the component would know
the information it contained as quickly as possible. Assume that messages will now spread in
sequential timesteps using the following mechanism. If an individual is told the message at
timestep , the individual will forward the message to all of their direct connections at
timestep +1. Individuals can therefore be told the message more than once. From this,
answer the following questions using network analysis measures as part of the approach:
Q1. Assume that you have to choose 1 individual to tell the message to at timestep 0.
What set of individuals could you choose this individual from and how many
timesteps would be needed for everyone in the component to receive the message?
Q2. Assume that you have to choose 5 individuals to tell the message to at timestep 0.
Provide an example set of 5 individuals that would result in the message being
received by everyone in the component in fewer timesteps than in Q1.
Learning Outcomes Assessed
1. Analyse fundamental traits of complex networks by synthesising theoretical concepts
and methodologies from graph theory.
2. Evaluate and implement computational approaches to model and visualise complex
social phenomena.
3. Design and create software to investigate or support human interaction behaviour.
Criteria for assessment
Credit will be awarded against the following criteria. There are 100 marks available for this
assignment. Each of the 16 questions are worth 6 marks, split between up to 3 marks for the
approach and implementation and up to 3 marks for the explanations and justifications of
the approach and implementation. This totals 96/100 possible marks. Marks will be
awarded using the following criteria: