University of Colombo School of Computing — BIT External Degree
EN6106 — Emerging Topics in IT
Topic 4: Social Network Analysis
Practice MCQ Paper | Academic Level III — Semester 6
Questions:
25 MCQs
Total Marks:
100
Duration:
1 Hour
Pass Mark:
40 / 100
Penalty:
Yes
Important Instructions
This paper has
25 questions
covering all sub-topics of Lesson 4: Social Network Analysis.
Each question has
5 choices
with
ONE OR MORE
correct answers.
All questions carry
equal marks
(4 marks each, total 100 marks).
There is a
penalty for incorrect responses
to discourage guessing.
To
PASS
, you must score at least
40 marks out of 100
.
Click
Submit Paper
only when you have answered all questions.
Marking Scheme (per question — max 4 marks)
Score = [ (Correct choices selected ÷ Total correct choices) − (Wrong choices selected ÷ Total wrong choices) ] × 4
Minimum score per question = 0 | Maximum score per question = 4
Answered
0 / 25
Score:
— / 100
Submit Paper
4.1 – 4.2 Introduction & Goals of Analysis
Q 1
Which of the following statements correctly describe
Social Network Analysis (SNA)
?
4 marks
(a)
SNA uses mathematical and statistical tools to analyze relationships and interactions among individuals, groups, or entities.
(b)
SNA is exclusively applicable to criminal investigation and law enforcement only.
(c)
In SNA, each individual or entity is represented as a node or vertex, and connections are represented as edges or links.
(d)
SNA can only analyze directed graphs and cannot handle undirected relationships.
(e)
SNA is a field concerned with the mathematical analysis of how individuals and groups are connected within a network.
Question score:
—
Q 2
Which of the following
social phenomena
can Social Network Analysis be used to study?
4 marks
(a)
Communication patterns and influence within a group.
(b)
The spread of information or disease through a population.
(c)
Collaboration and social support among individuals.
(d)
Hardware performance benchmarking and computer speed testing.
(e)
How social change happens and how ideas or behaviors spread within communities.
Question score:
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Q 3
Which of the following statements about
networks
and their components are correct?
4 marks
(a)
Actors in a network are represented as nodes, vertices, or points.
(b)
Ties in a network are represented as edges, arcs, lines, or links.
(c)
A network can only represent family relationships and no other types.
(d)
Types of social relationships in a network include family, friendship, and customer relationships.
(e)
Networks connect pairs of actors through edges or ties.
Question score:
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Q 4
Which of the following correctly describe the
goals of Social Network Analysis
?
4 marks
(a)
To gain insight into a community by examining the relationships between its members.
(b)
To identify key individuals and groups within the network as well as the connections between them (components).
(c)
To replace all social connections within a community with machine-generated links.
(d)
Nodes in SNA represent people, and the links between them can include friendships, family ties, or financial relationships.
(e)
Networks consist of nodes (points) that are linked by connections.
Question score:
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Q 5
The
systematic approach
to Social Network Analysis provides numerous benefits over qualitative methods. Which of the following are correct?
4 marks
(a)
The approach is objective and replicable, and does not require extensive knowledge or training.
(b)
It can help differentiate between core and peripheral gang members for more targeted responses.
(c)
It completely replaces all traditional forensic science techniques.
(d)
The analysis can be centrally collected and provided to local teams to examine and manipulate networks as needed.
(e)
It can help understand local gang issues and can be used for community impact statements and interventions.
Question score:
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4.3 Variables, Relations & Graph Theory
Q 6
In Social Network Analysis, which of the following are
correct mappings
of network terminology?
4 marks
(a)
Networks are also referred to as Graphs.
(b)
Nodes are also called Vertices or Actors.
(c)
Links are also called Edges or Relations.
(d)
Clusters are also called Communities.
(e)
Edges are also called Matrices.
Question score:
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Q 7
Which of the following correctly describe features of the
Social Network Graph
used to visualize entity relationships?
4 marks
(a)
The Social Network Graph shows entity-to-entity links and relationship clusters.
(b)
Entity-to-entity links display all entities related to the main entity without the linking attributes.
(c)
Attribute information can be accessed through the Attribute Explorer tool.
(d)
Relationship clusters group related entities and help identify patterns among clusters.
(e)
The Social Network Graph can only display undirected, non-clustered networks.
Question score:
—
Q 8
Which of the following statements correctly define a
graph
and its components?
4 marks
(a)
A graph is a visual representation of a set of objects with links connecting pairs of objects.
(b)
Objects in a graph are represented as points called vertices.
(c)
The links between objects in a graph are called edges.
(d)
A graph consists of a pair of sets (V, E) where V is the set of vertices and E is the set of edges.
(e)
In a graph, V is the set of edges and E is the set of vertices connecting pairs of nodes.
Question score:
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Q 9
Which of the following correctly describe fundamental
graph properties
used in Social Network Analysis?
4 marks
(a)
Two vertices are adjacent if they are connected to each other through an edge.
(b)
A path is a sequence of edges (or nodes connected by edges) between two vertices.
(c)
Degree refers to the number of edges incident to a node.
(d)
An edge can only connect three or more vertices simultaneously.
(e)
Each vertex in a graph can be identified using an array index.
Question score:
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Q 10
Which of the following statements about
cluster analysis
in graphs are correct?
4 marks
(a)
Cluster analysis is important in detecting graph elements with "similar" properties, especially in large networks.
(b)
Similarity measures are usually calculated based on topological criteria, node location, or other characteristics of graph elements.
(c)
Each cluster contains elements that share common properties and characteristics.
(d)
The collection of all clusters in a network forms a clustering.
(e)
All clusters in a network must contain an equal number of nodes.
Question score:
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4.4 Mathematical Foundations & Centrality
Q 11
Which of the following are part of the
Mathematical Foundations
of Social Network Analysis?
4 marks
(a)
Graph Theory — the branch of mathematics most relevant to SNA.
(b)
Centrality Measures — used to determine the importance of nodes within a network.
(c)
Matrix Algebra — including adjacency, incidence, Laplacian and modularity matrices.
(d)
Quantum Computing — the primary computational tool underlying all SNA operations.
(e)
In graph theory, nodes represent individuals or groups and edges represent relationships between them.
Question score:
—
Q 12
Which of the following statements about
directed and undirected graphs
are correct?
4 marks
(a)
In a directed graph, the edges have a direction (represented graphically as arrows).
(b)
In an undirected graph, the edges do not have a direction and both nodes are treated interchangeably.
(c)
An undirected edge is an ordered pair of nodes that represents direction between two vertices.
(d)
Out-degree is the number of edges leaving a vertex in a directed graph.
(e)
In-degree is the number of edges entering a vertex in a directed graph.
Question score:
—
Q 13
Which of the following are well-known
centrality measures
used in Social Network Analysis?
4 marks
(a)
Degree Centrality
(b)
Betweenness Centrality
(c)
Eigenvector Centrality
(d)
Openness Centrality
(e)
Closeness Centrality
Question score:
—
Q 14
Which of the following describe the
importance of centrality
in Social Network Analysis?
4 marks
(a)
Centrality measures identify influential individuals, information brokers, and key connectors in a network.
(b)
Centrality provides insights into information flow, power distribution, and control within the network.
(c)
All centrality measures produce identical results and capture the same aspect of node importance.
(d)
Centrality analysis finds applications in social sciences, organizational studies, marketing, and public health.
(e)
Centrality analysis enhances our understanding of network structure, dynamics, and key actors.
Question score:
—
Q 15
Which of the following statements correctly describe
Degree Centrality
?
4 marks
(a)
Degree centrality measures the number of connections or links a node has.
(b)
Nodes with high degree centrality are highly connected within the network.
(c)
Degree centrality measures how quickly a node can reach all other nodes in the network.
(d)
Nodes with high degree centrality may have a greater influence or access to information.
(e)
Degree centrality is directly related to the number of edges incident to a node.
Question score:
—
Q 16
Which of the following statements correctly describe
Betweenness Centrality
?
4 marks
(a)
It measures the extent to which a node lies on the shortest paths between other nodes.
(b)
Nodes with high betweenness centrality act as bridges, controlling the flow of information.
(c)
High betweenness centrality nodes connect different parts of the network and control information flow between them.
(d)
Betweenness centrality measures the importance of a node based solely on the importance of its neighboring nodes.
(e)
Betweenness centrality is used to identify nodes that serve as intermediaries between different parts of a network.
Question score:
—
Q 17
Which of the following statements accurately describe
Eigenvector Centrality
?
4 marks
(a)
It considers both the number of connections a node has and the importance of those connections.
(b)
Nodes with high eigenvector centrality are connected to other important nodes in the network.
(c)
It assigns relative scores to all nodes in the network, indicating their overall influence or prestige.
(d)
Eigenvector centrality only counts the number of direct connections a node has, ignoring the importance of those connections.
(e)
High eigenvector centrality indicates that a node has overall influence or prestige within the network.
Question score:
—
Q 18
Which of the following statements correctly describe
Closeness Centrality
?
4 marks
(a)
It measures how quickly a node can access or reach other nodes in the network.
(b)
Nodes with high closeness centrality have shorter average path lengths to other nodes.
(c)
High closeness centrality enables efficient information flow within the network.
(d)
Closeness centrality measures the number of edges directly incident to a node, not path lengths.
(e)
Closeness centrality is directly related to the average path length a node has to all other nodes in the network.
Question score:
—
Q 19
Which of the following statements correctly describe the
Adjacency Matrix
in graph representation?
4 marks
(a)
An adjacency matrix is a square matrix that represents a graph using boolean values (0's and 1's).
(b)
Each element in the adjacency matrix indicates whether there is a direct edge or path between two vertices.
(c)
In an adjacency matrix, a value of 1 indicates a direct connection between two vertices, and 0 indicates no connection.
(d)
The adjacency matrix has a row for every edge and a column for every vertex in the graph.
(e)
The adjacency matrix is part of the Matrix Algebra foundations used in Social Network Analysis.
Question score:
—
Q 20
Which of the following statements correctly describe the
Incidence Matrix
?
4 marks
(a)
For an undirected graph G with n vertices and m edges, the incidence matrix is an n × m matrix.
(b)
In the incidence matrix, there is a row for every vertex and a column for every edge.
(c)
The number of ones in an incidence matrix of an undirected graph (without loops) equals the sum of the degrees of all vertices.
(d)
An incidence matrix is always a square matrix with equal numbers of rows and columns.
(e)
In the incidence matrix, c
ij
= 1 if vertex V
i
is incident by edge e
j
, and 0 otherwise.
Question score:
—
4.5 – 4.6 Data Collection & Data Management
Q 21
Which of the following are recognized
methods of data collection
for Social Network Analysis?
4 marks
(a)
Surveys — asking respondents to identify their connections and describe their relationships.
(b)
Observation — directly observing individuals or groups and recording their relationship dynamics.
(c)
Online data collection — gathering data from platforms like social media, online communities, and forums.
(d)
Blockchain mining — the only legitimate method for gathering social network data.
(e)
Specialized methods such as snowball sampling, name generators, and name interpreters.
Question score:
—
Q 22
Which of the following correctly describe the use of
surveys
and
specialized methods
in social network data collection?
4 marks
(a)
Surveys allow data collection on a large number of individuals and relationships.
(b)
A limitation of surveys is that they are subject to biases such as social desirability and recall bias.
(c)
Snowball sampling starts with a few individuals and expands the network through their connections.
(d)
Surveys provide completely unbiased and fully objective data with no limitations.
(e)
Name generators ask respondents to list their contacts, while name interpreters have respondents interpret those contacts.
Question score:
—
Q 23
Data Management
in Social Network Analysis is crucial for several reasons. Which of the following are correct?
4 marks
(a)
Data Organization — structuring data coherently to analyze relationships in the network effectively.
(b)
Data Accuracy — standardizing data, removing errors and duplicates, and ensuring trustworthy results.
(c)
Data Documentation — transparent documentation to enhance sharing, collaboration, and replicability.
(d)
Effective data management eliminates the need for any data collection process.
(e)
Data Security — protecting sensitive information and adhering to ethical guidelines and regulations.
Question score:
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4.7 – 4.8 Multivariate Analysis & Visualizations
Q 24
Which of the following are recognized types of
Multivariate Analysis
techniques used in Social Network Analysis?
4 marks
(a)
Regression Analysis — examining relationships between two or more variables to understand how changes in one relate to another.
(b)
Factor Analysis — identifying underlying factors or dimensions by reducing data complexity through grouping similar variables.
(c)
Multivariate analysis is not applicable to social network analysis and is only used in physical sciences.
(d)
Cluster Analysis — grouping individuals or nodes based on similarities in multiple variables to identify subgroups or communities.
(e)
Multidimensional Scaling — visualizing relationships between variables and identifying patterns or clusters.
Question score:
—
Q 25
Which of the following are recognized
visualization techniques
used in Social Network Analysis?
4 marks
(a)
Node-Link Diagrams — depicting nodes as circles/dots and relationships as lines/edges, revealing overall network structure.
(b)
Matrix Diagrams — visual representation of node relationships in a matrix format, useful for uncovering patterns and clusters.
(c)
Heat Maps — matrix diagrams using color to represent the strength or intensity of relationships between nodes.
(d)
Force-Directed Layouts — positioning nodes based on their relationships using algorithms to unveil clusters and subgroups.
(e)
Visualization techniques are not relevant to Social Network Analysis and serve no analytical purpose.
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