*Social Network Question Bank*
Q-1] Describe Datasets and its formats (CSV, GML, GEFX, Pajek.Net, GraphML).
Datasets:
1] A data set is an ordered collection of data.
2] This set is normally presented in a tabular pattern.
3] A collection of information obtained through observations, measurements,
study, or analysis is referred to as data.
4] Data can be organized in the form of graphs, charts, or tables.
5] Datasets are collections of structured or unstructured data that are organized and
stored for analysis, processing, and use in various applications.
6] Datasets can contain a wide range of information, such as text, images, audio,
video, numerical values, or a combination of these.
7] A dataset is a set of numbers or values that certain to a specific topic.
8] The data set consists of data of one or more members corresponding to each
row.
Types of Datasets:
Types of data set as follows,
1] Numerical data sets
2] Bivariate data sets
3] Multivariate data sets
4] Categorical data sets
5] Correlation data sets
Formats of Datasets:
Some Formats of datasets are as follows:
1] CSV (Comma Separated Value):
It has extension either .txt or .csv. CSV format file can have 2 more types it
can be either edge list or adjacency list format.
1] Edge list Format:
Basically, it can edge and weights if required. Every row contains 2
nodes, first node will be the source node and the second node will be the target
node.
2] Adjacency list format:
Basically every contains 2 or more nodes. The first node is the source node
and subsequent nodes in the same row are the nodes connected directly to source
node like in first row 1 is directly connected to 2, 5 and 7 nodes.
2] GML (Graph Modelling Language):
It is the most commonly used format for network datasets because it
provides flexibility for assigning attributes to the nodes and edges.
3] GEXF (Graph Exchange XML Format):
It was created by Gephi people. Gephi is an open source software that is used
for visualizing and analyzing social networks. This format is also inspired by XML
as it has similar tags.
4] Pajek .net:
It has extension .NET or .Paj .It is widely used for network datasets. For
every row, you have every node return and all nodes are done you start with
information about edges that contain source node and the target node.
5] Graph ML:
Here ML stands XML as it is very much similar to XML. As in XML, there
are hierarchical structures and their tags. Similarly in graphml also there are tags
like XML tag, graphml tag, graph tag, node tag, and edge tag.
Q-2] Illustrate Granovetter's strength of weak ties.
Granovetter’s Strength of Weak ties:
1] Granovetter's strength of weak ties is a sociological concept that
highlights the importance of weak ties or connections in social
networks.
2] Developed by sociologist Mark Granovetter in 1973.
3] The theory challenges the prevailing belief that strong ties are the most
influential in social networks.
4] Weak relationship is caused by distant social relationships and very
infrequent meetings or interactions.
5] Weak ties are typically composed of people who are less like us and
who have access to different social circles and networks.
6] Granovetter's strength of weak ties theory highlights the importance of
maintaining and leveraging weak ties in social networks.
Advantages of Weak Ties:
1] Access to diverse information
2] Broader opportunities
3] Enhanced social capital
4] Information diffusion and influence:
5] Reduced redundancy
6] Increased social integration
Q-3] State emergence of connectedness.
Emergence of connectedness in social network:
1] The emergence of connectedness is to check whether the graph is
connected or not.
2] It says that in a graph of N nodes, we just need NLogN edges to make
graph connected.
3] The emergence of connectedness in social networks refers to the
process by which individuals establish and maintain connections with
others, creating a network of relationships.
4] Connectedness is a fundamental aspect of social networks and plays a
crucial role in shaping the dynamics, interactions, and information
flow within the network.
5] The emergence of connectedness in social networks is a complex and
dynamic process.
6] It is influenced by various individual, social, and environmental
factors.
Factors of Emergence of connectedness:
There are several factors that contribute to the emergence of connectedness in
social networks:
1] Human interaction
2] Common interests and goals
3] Proximity and physical proximity
4] Social ties and network effects
5] Technology and digital platforms
6] Social norms and societal structures
Advantages of Emergence of Connectedness:
1] Enhanced Communication
2] Global Reach
3] Access to Information and Education
4] Economic Growth
5] Efficiency and Productivity
6] Innovation and Creativity
7] Environmental Sustainability
Q-4] Illustrate Triadic Closure and clustering coefficient.
1] Triadic Closure:
1] Triadic closure is a concept in social network theory.
2] It was suggested by German sociologist Georg Simmel in 1908.
3] Triadic closure is the property among three nodes A, B, and C.
4] If the connections A-B and A-C exist, there is a tendency for the new
connection B-C to be formed.
5] Triadic closure can be used to understand and predict the growth of
networks.
6] It is only one of many mechanisms by which new connections are
formed in complex networks.
2] Clustering Coefficient:
1] A clustering coefficient is a measure of the degree to which nodes in a
graph tend to cluster together.
2] The clustering coefficient is a measure used in network analysis.
3] It is used to quantify the degree of clustering or connectivity
within a
network.
4] It provides insight into how closely connected nodes are to their
neighboring
nodes.
5] In simple terms, the clustering coefficient measures the tendency of nodes
in a network to form clusters or groups.
6] It quantifies the likelihood that two neighbors of a given node are also
connected to each other.
Q-5] State Neighborhood Overlap.
Neighborhood Overlap:
1] Neighborhood overlap, in the context of a social network, refers to the
degree of similarity or shared connections between the immediate
neighbors of two nodes within the network.
2] It measures how closely connected the neighborhoods of two nodes.
3] Neighborhood overlap examines the extent to which two individuals in
a social network share common friends or connections.
4] It focuses on the overlap or similarity between the connections of their
immediate neighbors.
5] Neighborhood overlap can provide insights into the structure of social
networks.
6] It helps identify nodes that are likely to have similar interests or share
information within the network.
To calculate neighborhood overlap between two nodes, you can follow these steps:
1] Select two nodes of interest in the social network.
2] Identify the immediate neighbors of each of the two nodes.
3] Determine the number of shared neighbors between the two nodes.
4] Calculate the neighborhood overlap using the formula:
Neighborhood overlap = (number of shared neighbors) / (total number of distinct
neighbors)
Q-6] Discuss Embededness, Structural Holes, and Social Capital.
1] Embededness:
1] Embeddedness in a social network refers to the extent to which an
individual or a relationship is interconnected or embedded within a
larger social context.
2] It measures the degree of connections, ties, or relationships.
3] Embeddedness looks at how deeply someone or something is
integrated into a social network.
4] It considers the connections and relationships that surround and
influence an individual or a specific relationship.
5] There are two types of embeddedness: Individual Embeddedness and
Relationship Embeddedness.
2] Structural Holes:
1] Structural holes in social networks refer to gaps or missing connections
between individuals or groups within a network.
2] These gaps represent opportunities for information flow, influence, and
control within the network.
3] Structural holes highlight the spaces or holes in a social network.
4] It highlights where connections could potentially exist but are currently
absent.
5] These gaps can be advantageous or disadvantageous depending on how
they are utilized.
3] Social Capital:
1] Social capital in social networks refers to the resources, benefits, and
advantages.
2] It represents the value that comes from social interactions, cooperation,
and social support within a community.
3] It highlights the importance of building and leveraging social
relationships to enhance well-being, opportunities, and collective
outcomes.
4] Social capital is built on trust and shared norms within a network.
5] Social capital facilitates the flow of information and influence within a
network.
Q-7] Explain Community Detection Using Girvan Newman Algorithm.
Community detection:
1] Detecting communities in a network is one of the most important tasks in
network analysis.
2] In a large scale network, such as an online social network, we could have
millions of nodes and edges.
3] Detecting communities in such networks becomes a herculean task.
4] Therefore, we need
community detection
algorithms that can partition
the network into multiple
communities.
Types of Community Detection:
A] Agglomerative Methods
B] Divisive Methods
Girvan-Newman Algorithm for Community Detection:
1] Under the Girvan-Newman algorithm, the communities in a graph are
discovered by iteratively removing the edges of the graph, based on the
edge betweenness centrality value.
2] The edge with the highest edge betweenness is removed first.
3] There are two ways:
• Understanding the Edge Betweenness Centrality
• Community Detection in Python
Understanding the Edge Betweenness Centrality:
1]
The edge betweenness centrality (EBC) can be defined as the
Number of shortest paths that pass through an edge in a network.
2]
Each and every edge is given an EBC score based on the
shortest paths among all the nodes in the graph.
3]
With respect to graphs and networks, the shortest path means
the path between any two nodes covering the least amount of distance.
Q-8] Illustrate Tie Strength, Social Media and Passive Engagement.
1] Tie Strength:
1] Tie strength refers to the level of intimacy, closeness, and strength of a
relationship between individuals.
2] It is often used to describe the strength of connections in social networks.
3] Here is an illustration of tie strength, social media, and passive
engagement.
Now let us consider two scenarios:
1] Strong Tie and Active Engagement:
In this scenario, two individuals, Alex and Beth, have been close friends for
many years.
They have a strong tie characterized by trust, emotional support, and
frequent face-to-face interactions.
They actively engage with each other on social media by regularly liking,
commenting, and sharing each other's posts.
2] Social Media and Passive Engagement:
In this scenario, two individuals, Chris and Dave, are acquaintances who
met at a conference.
They connected on a social media platform, but they don't have a strong
personal bond.
Their tie strength is relatively weak as they don't interact frequently or have
a deep understanding of each other's lives.
Q-9] Explain Homophily.
1] Homophily is the tendency in social groups of similar people
connected together?
2] We often hear similar voices interact with like-minded people.
Homophily has a significant impact on social media.
3] Birds with feather flock together.
1]
Assume there are 1000 people in a party out of which 500 are of age
ranges from 18-25 and the other 500 people are from the age group 40-50.
2]
So mathematically, if we pick any friendship randomly then mostly it
will be one teenager and one middle-aged person, which is most probable
condition.
3]
But we all know that teenagers may want to talk to a teenager and
middle-aged may want to talk to middle-aged people. This is known as
homophily.
Social Influence:
It is the tendency in which people change their attitude or behavior to meet
the social environment by getting influenced by other people is called Social
Influence.
Example:
Smoking.
Drinking.
Example of Social Influence:
Suppose I have a friend who smokes so he will influence me to smoke. This
is Social Influence.
Selection:
It is the tendency in which people make friends with similar interests i.e.
people select other people having similar habits or interests.
Example:
Two people who speak the same language.
Suppose there is a person who speaks Spanish and I also know Spanish so I will
select that person and talk to him. This is called Selection.
Q-10] Compare Selection and Social Influence.
Points
Selection
Social Influence.
1.
Definition
Suppose there is a person who
speaks Spanish and I also know
Spanish so I will select that person
and talk to him. This is called
Selection
It is the tendency in which people
change their attitude or behavior to
meet the social environment by
getting influenced by other people is
called Social Influence.
2.
Work
Social Influence makes connected In selection, people select similar
nodes similar.
nodes and connects with them.
3.
Example
Suppose there is a person who
speaks Spanish and I also know
Spanish so I will select that person
and talk to him
Suppose I have a friend who smokes
so he will influence me to smoke.
5. Nature
It is an individual-driven process
It is a social-driven process.
6.
DecisionMaking
7.
Experience
It is an internal process guided by
personal goals and desires.
It is an external process that can sway
decision-making
It can be influenced by personal
experiences, knowledge, values,
and desires.
It primarily operates in individual
decision-making contexts.
It can be explicit or implicit.
4.
Diagram
8.
Context
It operates in social contexts.
Q-11] Classify Foci Closure and Membership Closure.
Foci closure and membership closure are terms commonly used in
mathematical analysis and topology to describe certain properties of sets or
functions.
1] Foci Closure:
1] The term foci closure seems to be a combination of two separate
concepts:
foci and closure.
2] However, there is no specific mathematical concept known as foci
closure. Without further context or clarification, it is difficult to provide a
specific definition or classification for this term.
2] Membership Closure:
1] Membership closure, also known as closure under membership, refers to a
property of sets or collections of objects.
2] A set is said to have membership closure if it contains all of its members.
In other words, for every element in the set, that element is also an
element of the set itself.
For example, let's consider a set A = {1, 2, 3}. If we take any element from A, say
2, we observe that 2 is indeed an element of A. Therefore, A has membership
closure.
In mathematical terms, membership closure is often denoted as follows:
For a set A, if x belongs to A (x ∈ A), then x is also an element of the closure of A.
Membership closure is a fundamental concept in topology, where it is used to
define closed sets.
A set is said to be closed if it contains all its limit points, which is essentially a
form of membership closure.
Q-12] Give brief Introduction to Fatman Evolutionary model.
1] In the Fatman evolutionary model, there are 3 main concepts to make an
evolutionary model i.e.
1. Homophily
2. Social influence
3. Closure
1] Homophily:
People who are similar to each other, they tend to make friends with each
other.
2] Social Influence:
People change their behavior and properties because of social influence.
3] Closure:
There are 3 main kinds of closures i.e. triadic closure, membership closure,
And foci closure.
1] Triadic closure- In triadic closure, if A is a friend of B and B is a
friend of C then C will eventually become friends of A.
2] Membership closure- In membership closure, if A and B are in the
same club then there is a tendency that they will become friends.
3] Foci closure- It is the probability of becoming friends when they have
the same foci.
Example:
So, According to Fatman Hypothesis, beware of fat friends, if your friend is fat
then the probability of you gaining the weight becomes high.
Now the below code will show you the implementation of the above 3
concepts using the evolutionary model in python by taking an example.
Assume there is a city with a number of people, and we have everyone’s BMI.
Then we will see as evolution continues people having similar BMI become
friends to each other which is homophily.
Algorithm:
1. Create a graph with say N nodes,
2. Add edges and labels for each node.
3. Now add social foci and social foci label for each node
4. Now implement homophily.
5. After that implement closure.
6. At last implement social influence.
7. Now visualize the graph.
Q-13] Spatial Segregation: An Introduction
1] Segregation refers to the act of separation of people from others or the
main group.
2] Spatial Segregation refers to the distribution of social groups or any
other elements in space.
3] In Spatial Segregation, people tend to migrate to other places where
they have more neighbors who are like them.
4] According to spatial segregation, it is unlikely that a person can stay
at a place where the neighbors are very different from you and you cannot
have a good conversation with your neighbor.
5] A person will more likely to migrate to a place where his neighbors
are similar to them and you can have a good conversation with your
neighbor.
Example:
1] Assume you went to a foreign country for living and you are deciding
where to take the house.
2] So you will more likely to choose a place where your neighbors are
from your country and speaks the same language as yours and not a place
where people are from a different country and speaks a different language.
This is spatial segregation.
Locality with all different neighbors from you
Locality with all same neighbors as you
3] But sometimes it is not possible to have all neighbors as same as you.
So for that, we can have some threshold value for similar neighbors to
stay there.
Q-14] Explain Structural Balance.
1. Structural balance refers to the tendency of relationships within a social
network to form balanced or imbalanced patterns.
2. It was introduced by psychologist Fritz Heider and expanded upon by
sociologist Leon Festinger.
3. Balanced relationships exhibit consistency, where positive ties align with
each other and negative ties align with positive ties.
4. Imbalanced relationships create conflict and inconsistency within the
network.
5. Individuals in a social network seek balance to reduce cognitive dissonance.
6. Balanced relationships promote harmony, stability, and cooperation within
the network.
7. Imbalanced relationships create tension and potential for change or
instability.
8. Structural balance can be observed in triads, where balanced triads have
harmonious configurations, and imbalanced triads exhibit conflicts.
9. Individuals may modify their relationships to reduce imbalances and restore
balance within the network.
10.Structural balance provides insights into social dynamics, conflict resolution,
and the formation of group norms within networked settings.
11.Structural balance can be used to analyse and understand various types of
networks.
12.Imbalances in relationships can lead to social tension, conflicts, and the
potential for the formation of new relationships.
Q-15] Characterizing the structure of balanced networks.
1] Interconnected Nodes:
1] Balanced social networks are characterized by interconnected nodes, where
entities in the network are connected to each other through various relationships.
2] These relationships can represent friendships, professional connections or other
forms of social interactions.
2] Reciprocity:
1] Balanced social networks often exhibit reciprocity, meaning that if
person A is connected to person B, there is a high likelihood that person
B is also connected to person A.
2] Reciprocal connections reflect mutual relationships.
3] Clustering:
1] Balanced social networks tend to exhibit clustering, where individuals
within the network form groups.
2] These clusters can represent social circles, communities of interest, or
shared affiliations.
4] Homophily:
1] Balanced social networks often display homophily.
2] This characteristic indicates the tendency of individuals to associate
with others who are similar to them.
5] Small-World Properties:
1] Balanced social networks can exhibit small-world properties.
2] This means that the network has a high level of local clustering, where
individuals tend to be connected to their immediate neighbors.
6] Structural Balance:
1] Balanced social networks often exhibit structural balance, a concept
derived from social psychology.
2] Structural balance theory suggests that individuals in a network prefer
balanced relationships.
3] This balance contributes to network stability.
Q-16] Discuss Balance theorem.
1] The Balance Theorem, also known as the Heider Balance Theory, is a
concept that relates to the psychological balance within social
relationships.
2] The theorem was introduced by Fritz Heider in the 1940s and has
since been widely studied in the field of social psychology.
Balance Theorem in the context of social networks as follows:
1] Triads and Signed Relationships:
1] The Balance Theorem focuses on triads, which are groups of three
individuals connected through relationships.
2] These relationships can be positive or negative. Each relationship is
assigned a sign (+ or -) to indicate its positive or negative nature.
2] Balance and Imbalance:
1] According to the Balance Theorem, a balanced triad is one in which
the signs of the relationships form a harmonious pattern.
2] These balanced patterns contribute to psychological harmony within
the social network.
3] Imbalanced Triads:
1] On the other hand, an imbalanced triad is one in which the signs of the
relationships create a discordant or conflicting pattern.
2] Imbalanced triads can consist of two negative relationships.
4] Motivation for Resolution:
1] The Balance Theorem suggests that individuals are motivated to
resolve imbalanced triads in order to restore balance.
2] The tension arising from imbalanced relationships generates a
psychological drive to reduce the imbalance.
5] Network Dynamics:
1] The Balance Theorem has implications for the dynamics of social
networks.
2] The resolution of imbalanced triads can lead to changes in
relationship dynamics.
6] Applications in Social Influence:
1] The Balance Theorem has been applied to various aspects of social
influence.
2] It helps explain how social networks influence individual attitudes and
behaviors through the pursuit of balance.
It is important to note that the Balance Theorem provides a theoretical
framework for understanding the psychological tendencies in social relationships.
Q-17] Introduction to positive and negative edges.
In social network analysis, positive and negative edges refer to the types of
relationships or connections between individuals in a network.
1] Positive Edges:
1] Positive edges represent positive relationships or connections between
individuals in a social network.
2] These relationships can include friendships, support, or any other
positive interaction.
3] Positive edges indicate that individuals have a favorable or positive
attitude towards each other.
Examples of Positive Edges:
1] Positive edges can manifest in various ways in social networks. For
instance, in a friendship network.
2] Positive edges can also denote supportive relationships.
Characteristics of Positive Edges:
1] Positive edges are typically associated with mutual liking, trust, and
the exchange of positive emotions.
2] They contribute to the formation of social bonds, social support
networks, and the spread of positive influence within a social network.
Positive Edges- Green
Negative Edges-Red
2] Negative Edges:
1] Negative edges represent negative relationships or connections
between individuals in a social network.
2] These relationships can include conflicts, dislikes, disagreements, or
any other form of negative interaction.
3] Negative edges indicate that individuals have an unfavorable or
negative attitude towards each other.
Examples of Negative Edges:
1] Negative edges can arise in social networks in various contexts. In a
dislike network, negative edges represent individuals who have expressed
aversion.
2] Negative edges can also indicate conflicts or disagreements
relationships between individuals.
Characteristics of Negative Edges:
1] Negative edges are associated with distrust, hostility, negative
emotions, and potential social tension within a network.
2] They can impact the overall dynamics of a social network.
Q-18] Moving a network from an unstable to stable state.
Moving a network from an unstable to a stable state in social network
dynamics involves transitioning the network's properties, relationships, or
dynamics.
1] Identify Instability:
1] First, it's important to identify the factors or characteristics that
contribute to the network's instability.
2] This can include the presence of conflicts, polarization, or other
dynamics that hinder network stability.
2] Foster Positive Relationships:
1] Promote the formation and strengthening of positive relationships
within the network.
2] Encourage individuals to build trust, cooperation, and collaboration
with each other.
3] Resolve Conflicts:
1] Address and resolve conflicts or negative relationships within the
network
2] Resolving conflicts helps reduce volatility and promotes stability
within the network.
4] Enhance Communication:
1] Improve communication channels and encourage effective
information flow within the network.
2] Open and transparent communication helps prevent
misunderstandings, facilitates coordination, and promotes a sense of
shared understanding and common goals.
5] Establish Norms and Rules:
1] Develop and enforce clear norms, rules, or guidelines for behavior
within the network.
2] Expectations help set boundaries, promote positive interactions, and
create a stable social environment.
6] Monitor and Adapt:
1] Continuously monitor the network's dynamics, relationships, and
stability.
2] Stay attuned to changes and proactively address emerging issues or
potential sources of instability.
3] Adapt the network's strategies, structures, or interventions based on
feedback.
Q-19] Discuss Web Graph and collecting web graph.
Web Graph:
1] The web graph refers to the interconnected structure of web pages
on the World Wide Web.
2] It represents the network of hyperlinks between web pages, where
each page is represented as a node, and the links between pages are
represented as edges.
-Directed Web GraphDirected Graph:
1] The web graph is typically represented as a directed graph since web
page links have a direction from the source page to the destination page.
2] This directionality captures the relationship between web pages,
indicating which pages link to others.
PageRank:
1] The web graph is closely associated with the PageRank algorithm.
2] PageRank assigns a numerical importance score to each web page
based on the number and quality of incoming links.
Collecting Web Graph Data:
1] Web Crawlers: To collect web graph data, web crawlers, also known
as web spiders or web robots, are used. These automated programs
systematically browse the web.
2] Starting Points: Web crawlers typically start from a set of seed URLs,
which can be popular websites, search engine results, or other sources.
3] Link Extraction: As web crawlers visit web pages, they extract links
present on those pages. These links can be found in the HTML source
code.
URL Frontier:
1] To determine which pages to visit next, web crawlers maintain a URL
frontier or queue.
2] The frontier stores a list of URLs to be visited in the future, based on
the priority assigned to each URL.
Scale and Challenges:
1] Collecting the entire web graph is a massive undertaking due to the
sheer size and constant growth of the web.
2] Challenges include dealing with dynamic pages, handling duplicates,
and managing web page updates.
Q-20] Compare Equal Coin Distribution and Random Coin Dropping.
1] Equal Coin Distribution:
1] Fairness:
1]
Equal coin distribution refers to a scenario where every individual in a
social network receives an equal number of coins or resources.
2]
It emphasizes fairness and ensures that each person has an equal
starting point or opportunity.
2] Equity:
1] Equal coin distribution promotes equity within the social network by
reducing inequalities.
2] It aims to provide an equal baseline for all individuals.
3] Resource Allocation:
1]
Equal coin distribution ensures that resources are distributed evenly
among network members.
2]
This can promote a sense of inclusivity, cooperation, and
collaboration within the network.
2] Random Coin Dropping:
1] Randomness:
1] Random coin dropping refers to a scenario where coins or resources
are distributed randomly among individuals in a social network.
2] It does not take into account any criteria or factors other than chance.
2] Variation:
1] Random coin dropping introduces variation in the distribution of
resources.
2] Some individuals may receive more coins, while others may receive
fewer, purely by chance.
3] Exploration and Adaptation:
1]
Random coin dropping can promote exploration and adaptation
within the network.
2]
It encourages individuals to interact with others, exchange
resources, and adapt to the varying distribution of coins.
3]
This dynamic environment can foster creativity, innovation, and
the emergence of new connections.
Q-21] Explain Google Page Ranking Using Web Graph.
1] Web Graph Representation:
1] Google PageRank algorithm utilizes the web graph, which represents
the interconnected structure of web pages and their links on the World
Wide Web.
2] In the web graph, each web page is represented as a node, and the
links between pages are represented as edges.
2] Importance of Links:
1] Google PageRank assigns importance or relevance scores to web
pages based.
2] The more incoming links a page has, the more important or
authoritative it is considered to be.
3] Voting System:
1] In the PageRank algorithm, the links between web pages can be seen
as votes of confidence.
2] When a page links to another page, it is essentially casting a vote for
that page's importance. The more votes a page receives, the higher its
PageRank score.
4] Importance Distribution:
1] PageRank distributes the importance received from incoming links to
the linked pages.
2] A page with a high PageRank score will pass on a proportionally
higher amount of importance to the pages it links to.
5] Iterative Calculation:
1] PageRank is calculated through an iterative process. Initially, each
page is assigned an equal starting PageRank score.
2] In each iteration, the PageRank scores are updated based on the
importance received from the linking pages.
6] PageRank as a Ranking Metric:
1] The final PageRank scores obtained after the iterative process
represent the relative importance or authority of each web page in the web
graph.
2] Google uses these PageRank scores, along with other factors, to rank
search results.
Q-22]
SR.
No.
Compare Degree Rank versus PageRank.
DegreeRank
PageRank
1
DegreeRank is a centrality measure PageRank is a centrality measure that
that ranks nodes based on their
ranks nodes based on their
degree
importance in the network.
2
DegreeRank simply counts the
number of connections a node has
and uses that count as its centrality
score.
PageRank calculates centrality
iteratively. Initially, all nodes are
assigned an equal score. In each
iteration, the score of each node is
updated based on the scores of its
incoming neighbors.
3
DegreeRank does not take into
PageRank considers the importance
account the importance of the
of the nodes that link to a particular
nodes to which a node is connected. node.
4
DegreeRank can be used in both
undirected and directed graphs
5
Nodes with higher degrees are Nodes with higher PageRank scores
considered more central.
are considered more central.
6
DegreeRank heavily relies on the
PageRank takes into account the
node degree as the sole criterion for entire network structure and the
centrality.
importance of nodes connected to a
particular node.
Q-23]
PageRank is primarily designed for
directed graphs.
why do we follow? Diffusion in Networks, Impact of Communities on.
In social networks, individuals often engage in following behaviors for
several reasons, and the impact of communities on diffusion plays a significant
role.
Why people follow and the impact of communities on diffusion in social
networks as follows:
1] Social Validation:
1] Following others in a social network provides a sense of social
validation.
2] By following individuals who are perceived as popular,
knowledgeable, or influential, people can feel more connected and
validated within their social circle.
2] Information Seeking:
1] Following others allows individuals to access a wealth of information
and resources.
2] People follow experts, thought leaders, or individuals with similar
interests to stay updated on relevant news, trends, and developments.
3] Social Influence:
1] Behavior is often driven by social influence.
2] When people observe others endorsing specific ideas, products, or
behaviors, they may be influenced to follow suit and adopt similar
attitudes or behaviors.
4] Community Identification:
1] Communities or groups within social networks often share common
interests, beliefs, or goals.
2] Communities can reinforce and amplify the diffusion of ideas and
behaviors within their members.
5] Network Effects:
Others can create network effects, where the value of being connected to
others increases as more people join the network.
6] Influence of Communities on Diffusion:
1) Communities within social networks can significantly impact diffusion
processes.
2) Communities often have established norms, values, and shared interests.
3) When a behavior or idea is endorsed or adopted by influential members
within a community.
4) Communities act as catalysts for diffusion, as they provide a concentrated
group of individuals.
Q-24] Discuss Cascade and Clusters.
1] Cascades:
1] A cascade refers to the spread or diffusion of information, behaviors,
or influence through a social network.
2] It involves the sequential adoption or propagation of a particular item
from one individual to another within the network.
3] Cascades are triggered by an initial adopter or influencer who adopts
and spreads a particular item within the network.
4] As this item spreads, it can activate other individuals in the network,
leading to subsequent adoptions and propagation.
5] Cascades can spread rapidly, reaching a large portion of the network,
or they can die out quickly with limited adoption.
2] Clusters:
1] Clusters in social networks refer to groups of nodes that are more
densely connected to each other within the network.
2] Clusters represent subgroups or communities within the larger social
network.
3] Clusters arise due to the presence of homophily.
4] Clusters can be characterized by shared beliefs, values, or common
activities.
5] Clusters exhibit high levels of structural cohesion, meaning that nodes
within a cluster are densely connected to each other through direct or
indirect links.
6] This cohesion creates strong ties between
individuals within the cluster, communication, and influence.
Q-25] Describe Knowledge, Thresholds and the Collective Action.
1] Knowledge:
1] In social networks, knowledge refers to the information,
understanding, and expertise that individuals possess and share within the
network.
2] It includes a wide range of topics, such as personal experiences,
factual data, opinions, news, and insights.
3] Knowledge in social networks can be shared through various forms of
communication, including text, images, videos, and links.
2] Thresholds:
1] Thresholds in social networks represent the minimum level of
acceptance or agreement required for an individual to adopt a specific
behavior or belief.
2] They are the tipping points that determine when an individual decides
to conform to a particular idea or take action based on the influence of
others.
3] Thresholds can depending on factors such as the nature of the
network, the relevance of the behavior, and the social dynamics within the
network.
3] Collective Action:
1] The Collective Action refers to the application of artificial intelligence
and machine learning algorithms to analyze social network dynamics and
predict collective actions or behaviors.
2] It involves studying patterns of interactions, information diffusion,
and the influence of thresholds within the network.
3] By examining these factors, the Collective Action can provide insights
into the likelihood and potential impact of collective actions within a
social network.
4] It can help identify influential individuals or groups and understand
the mechanisms that drive collective behavior.
Q-26] Describe Hubs and Authorities & Principle of Repeated Improvement.
1] Hubs and Authorities:
1] Hubs:
1]
In the context of social networks, hubs are individuals or entities that
have a high number of connections or links to other nodes within the
network.
2]
They act as central points of information dissemination and play a
crucial role in connecting different parts of the network.
3]
Hubs are typically associated with high visibility and influence within
the network due to their extensive connections.
2] Authorities:
1]
Authorities, on the other hand, are individuals or entities within a
social network that are considered highly knowledgeable in a specific
domain.
2]
They possess expertise, credibility, and a reputation for providing
accurate and reliable information.
2] Principle of Repeated Improvement:
The principle of repeated improvement in social networks refers to the
iterative process of refining and enhancing the quality of information, connections,
and interactions within the network over time.
1] Feedback loop:
1]
The principle of repeated improvement relies on the collection and
analysis of user feedback within the social network.
2]
Users provide feedback through various means such as ratings,
comments, likes, shares, and user behavior patterns.
2] This feedback is valuable for understanding user preferences, identifying
strengths and weaknesses, and guiding future improvements.
2] Iterative refinement:
1]
Based on the feedback received, social networks can make
iterative changes and improvements to enhance the user experience,
quality of information, and overall network dynamics.
2]
These refinements can include algorithmic adjustments, user
interface enhancements, and addressing issues of trust and authenticity.
3] Continuous adaptation:
1]
Social networks must adapt to changing user needs, emerging
trends, and evolving societal concerns.
2]
The principle of repeated improvement emphasizes the
importance of continuous adaptation to ensure that the network remains
relevant, engaging, and valuable to its users.
3]
This may involve incorporating new features, addressing
privacy concerns, combating misinformation, and promoting user
safety.
Q-27] Illustrate Page Rank Conservation and Convergence.
Page Rank:
1] Page Rank is an algorithm used to measure the importance or
relevance of web pages in a network, originally developed by Google.
2] It assigns a numerical value, known as Page Rank score, to each page
based on the quantity and quality of incoming links from other pages.
3] The more incoming links a page receives, especially from highly
ranked pages, the higher its Page Rank score.
1] Conservation:
1] Page Rank conservation refers to the principle that the total Page
Rank score in a social network remains constant over time, even as the
network evolves and new pages or nodes are added.
2] In other words, the sum of Page Rank scores across all pages in the
network remains unchanged.
2] Convergence:
1] Convergence in the context of Page Rank refers to the stabilization of
Page Rank scores over time.
2] Initially, Page Rank scores may vary significantly as the algorithm
goes through multiple iterations.
3] Conservation:
1] As the Page Rank algorithm iterates and recalculates the scores, the
total Page Rank score of the network remains constant.
2] Even if new pages are added or the link structure changes, the sum of
the Page Rank scores across all pages will always be the same.
Q-28] Explain Power Law, Appearance of Normal Distribution.
1] Power Law:
1]
Power law refers to a mathematical relationship where a variable's
frequency or magnitude is inversely proportional to its rank or size.
2]
In the context of social networks, power law is often observed in the
distribution of various characteristics, such as the number of connections or
links that nodes have.
-Power Law1] Node Degree Distribution:
• In social networks, the degree of a node represents the number of
connections or links it has with other nodes.
• The node degree distribution refers to the pattern of how these degrees
are distributed across the network.
2] Scale-Free Networks:
• A scale-free network is characterized by a few highly connected
nodes, known as hubs.
• In such networks, the node degree distribution follows a power law.
2] Appearance of Normal Distribution:
While the node degree distribution in social networks typically follows a
power law, it is important to note that when considering local properties of nodes
within the network, such as individual behaviors or attributes, the distribution may
often appear closer to a normal distribution.
1] Central Limit Theorem:
• The appearance of normal distribution in social networks can be
attributed to the Central Limit Theorem.
• According to this theorem, when a large number of independent and
identically distributed random variables are combined, their sum tends
to follow a normal distribution.
2] Individual Behavior and Normal Distribution:
• In social networks, the behaviors or attributes of individuals are
influenced by various factors.
• When these individual behaviors are combined or aggregated at the
network level, they tend to exhibit characteristics that align with a
normal distribution.
3] Emergence of Normality:
• As individual behaviors or attributes interact and propagate through
the social network, they tend to converge and blend together.
• This can be observed when examining traits such as opinions,
preferences, or traits.
Q-29] Illustrate Rich get richer phenomenon.
Rich get Richer Phenomenon:
1] In social networks, there is a phenomenon called Rich getting Richer
also known as Preferential Attachment.
2] In Preferential Attachment, a person who is already rich gets more and
more and a person who is having less gets less. This is called the Rich
getting richer phenomena or Preferential Attachment.
-Rich get Richer Phenomenon3] The "rich get richer" phenomenon refers to a situation where individuals or
entities who already possess wealth and resources.
Here are some key points to illustrate this phenomenon:
1]
Initial Advantage: The rich get a head start due to various factors like
inheritance, access to quality education or existing wealth.
2]
Compound Effect: Wealth and resources tend to generate additional
advantages, creating a compounding effect.
3]
Access to Opportunities: The wealthy have access to better opportunities,
such as exclusive investment opportunities, prestigious jobs, or business
connections.
4]
Network Effects: Being part of affluent circles enables access to influential
networks, where collaboration, partnerships, and mentorship opportunities arise.
5]
Education and Skills: High-quality education and skill development
programs are often costly.
6]
Systemic Factors: Socioeconomic systems, such as capitalism, can amplify
the rich get richer phenomenon.
Q-30] Discuss Epidemics, Simple Branching Process for Modeling Epidemics.
The simple branching process is a mathematical model used to understand
and analyze the spread of epidemics.
-Simple Branching processHere are some key points to discuss the concept of epidemics and the
application of the simple branching process for modeling them:
1] Epidemics:
1] An epidemic refers to the occurrence of a significant increase in the
number of cases of a particular disease within a population.
2] It often spreads rapidly and affects a large number of individuals
within a relatively short period.
2] Transmission Dynamics:
Epidemics are influenced by the transmission dynamics of the disease. This
includes factors such as the infectiousness of the disease and contact patterns.
3] Simple Branching Process:
1] The simple branching process is a stochastic model that assumes that
each infected individual has a fixed probability of transmitting the disease
to a certain number of susceptible individuals.
2] It provides a simplified framework for understanding the dynamics of
an epidemic.
4] Basic Reproduction Number (R₀):
The basic reproduction number represents the average number of secondary
infections caused by a single infected individual in a completely susceptible
population.
5] Branching Process Assumptions:
The simple branching process makes several assumptions, including
homogeneous mixing within the population.
6] Threshold Condition:
The branching process framework helps identify a threshold condition for
epidemic growth.
Limitations:
1] Complex
2] Spatial effects
Q-31] Discuss Basic Reproductive Number.
The Basic Reproductive Number (R₀) is a key epidemiological concept used
to measure the potential spread of infectious diseases within a population.
-Basic Reproductive Number-
Here are some important points regarding the basic reproductive number:
1] The Basic Reproductive Number, denoted as R₀ represents the average number
of new infections generated by a single infected individual in a completely
susceptible population.
2] R₀ reflects the inherent transmissibility or contagiousness of a disease.
3] It quantifies the potential for an infected individual to pass the infection on to
others and estimates the initial scale of an epidemic.
4] The value of R₀ depends on several factors, including the mode of transmission,
contact patterns within the population, and the susceptibility of individuals to
the disease.
5] R₀ can vary across different populations or settings due to variations in factors
like population density, healthcare infrastructure, and social behavior.
6] The duration for which an infected individual remains contagious influences R₀.
7] Longer infectious periods increase the potential for transmission and can result
in a higher value of R₀.
8] It's important to note that R₀ represents the average number of new infections
and does not consider individual-level variations.
Limitations:
1] Impact on evolving variants
2] Resource allocation, and
3] Outbreak control strategies
Q-32] Discuss SIR and SIS spreading models, Percolation model.
SIR and SIS spreading models, as well as the percolation model in social
networks as follows:
1] SIR Spreading Model:
-SIR Spreading Model1]
The SIR model is a commonly used epidemiological model that divides
individuals in a population into three compartments:
1] Susceptible (S)
2] Infected (I)
3] Recovered (R)
2]
It assumes that individual’s move from being susceptible to infected, and
then from infected to recovered, after which they gain immunity.
3]
In the SIR model, infected individuals can transmit the disease to susceptible
individuals through contact.
4]
The transmission rate is typically represented by the parameter β, which
determines the likelihood of infection per contact.
5]
Infected individuals recover from the disease and become immune to further
infection.
6]
The recovery rate is represented by the parameter γ, which determines the
average duration of the infectious period.
2] SIS Spreading Model:
-SIS Spreading Model1] The SIS model is another epidemiological model that classifies individuals into
two compartments:
1] Susceptible (S)
2] Infected (I)
2] Unlike the SIR model, individuals who recover from the infection in the SIS
model return to a susceptible state and can be re-infected.
3] Infected individuals in the SIS model can transmit the disease to susceptible
individuals, similar to the SIR model.
4] The transmission rate is typically denoted by the parameter β, representing the
likelihood of infection per contact.
5] In the SIS model, infected individuals do not gain immunity after recovery.
6] The SIS model is often used to study the dynamics of diseases.
3] Percolation Model:
-Percolation Model1]
The percolation model is a mathematical framework used to study the spread
of information, behaviors, or diseases on social networks.
2]
It focuses on the concept of network connectivity and the propagation of
information through the network.
3]
In the percolation model, nodes represent individuals or entities, and edges
represent connections or relationships between them.
4]
These connections can represent social ties, communication channels, or any
other form of interaction.
Q-33] Illustrate Small World Effect, Milgram's Experiment.
Illustration of the small world effect and Milgram's experiment in social
networks as follows:
1] Small World Effect:
-Small World Network1]
The small world effect refers to the phenomenon where individuals in a
large social network can be connected to one another through a relatively short
chain of social connections.
2]
In a small world network, individuals can reach each other through a few
intermediate connections, despite the large size of the network.
3]
The small world effect is often associated with the concept of "six degrees of
separation".
4]
The small world effect arises due to a combination of clustering and shortcut
connections in social networks.
5]
The small world effect highlights the interconnectedness and potential reach
of social relationships.
2] Milgram's Experiment:
1]
In the 1960s, social psychologist Stanley Milgram conducted a
groundbreaking experiment to test the small world effect.
2]
The experiment aimed to investigate the average path length between
individuals in the United States.
Procedure of Milgram's Experiment:
1] Milgram selected a group of participants who were instructed to send a
package to a target person they did not personally know.
2] The participants were only allowed to forward the package to someone
they knew on a first-name basis.
3] This process created a chain of social connections.
Criticisms and Limitations:
1]
Milgram's experiment has faced criticisms over the years, such as
concerns about the sample representativeness and the generalizability of the
findings to diverse populations.
2]
The study remains influential and has stimulated extensive research
on network structure.
Q-34] Discuss Myopic Search.
Myopic search refers to a decision-making strategy used by individuals in
social networks to navigate and obtain information.
Myopic search in social network as follows:
1] Myopic search is a local and short-sighted decision-making strategy in
which individuals in social networks primarily focus on their immediate
neighbors.
2] When faced with a question, problem, or need for information,
individuals employing myopic search tend to reach out directly to their
immediate contacts.
3] Myopic search offers some advantages in terms of efficiency and
convenience.
4] It allows individuals to quickly access information or resources from their
immediate network, saving time and effort compared to exploring distant.
5] Myopic search may discourage individuals from exploring outside of their
immediate network, opportunities, or resources that may exist in more
distant parts of the network.
6] The structure of the social network plays a crucial role in the effectiveness
and consequences of myopic search.
7] Myopic search is a prevalent decision-making strategy in social networks.
Advantages of Myopic Search:
1] Efficiency
2] Reduced Information Overload
3] Social Validation
Disadvantages of Myopic Search:
1] Limited Information Diversity
2] Limited Network Exploration
Q-35] How to be viral.
1]
Becoming viral in a social network is a goal for many individuals or
organizations looking to gain widespread attention and engagement.
2]
While virality is unpredictable and not guaranteed, there are certain
strategies that can increase your chances.
How to potentially achieve viral success in a social network as follows:
1] Compelling Content:
A] Create high-quality, engaging, and shareable content that resonates with
your target audience.
B] Focus on storytelling, emotional appeal, or novelty to capture people's
attention and make your content memorable.
C] Use visuals, such as images, videos, or info graphics, to enhance the
appeal and share ability of your content.
2] Understand Your Audience:
A] Conduct thorough research to understand the demographics, preferences,
and interests of your target audience.
B] Tailor your content to meet their specific needs and desires.
C] Use language, references, and cultural elements that resonate with your
audience to establish a connection.
3] Embrace Trends and Timeliness:
A] Stay updated with the latest trends, news, and events relevant to your
industry or niche.
B] Be timely in your content creation and distribution to ride the wave of
interest around specific topics.
4] Optimize for Social Sharing:
A] Make it easy for people to share your content by adding social sharing
buttons to your website or content platforms.
B] Craft catchy and attention-grabbing headlines or descriptions for your
content, increasing the likelihood of clicks and shares.
5] Engage with Your Audience:
A] Actively engage with your audience by responding to comments,
messages, and mentions.
B] Encourage and foster discussions around your content, which can lead
to increased visibility and reach.
6] Seed Initial Exposure:
A] Share your content across your own social media channels and
personal networks to give it an initial boost.
B] Reach out to friends, colleagues, or existing supporters and ask them
to share your content with their networks.