Definitions

Question 1

Node

Answer 1

An actor or entity in the network. Also known as ‘vertex’, ‘point’, ‘site’, ‘actor’, ‘entity’, etc

Question 2

Dyad

Answer 2

A pair of two nodes

Question 3

Edge

Answer 3

A connection between two nodes. Also known as ‘line’, ‘arc’, ‘link’, ‘tie’, ‘relationship’, ‘connection’

Question 4

Network

Answer 4

A set of nodes and a set of edge between those nodes

Question 5

Degree

Answer 5

The degree of a node is the number of edges connected to that node

Question 6

Directed Network

Answer 6

A network where all edges have a particular direction, so an edge from node v to node u is not the same as an edge from node u to node v

Question 7

Undirected Network

Answer 7

A network where edges do not have a particular direction, so an edge from node v to node u is also an edge from node u to node v

Question 8

Indegree

Answer 8

For a directed network, the indegree of a node is the number of incoming edges to that node

Question 9

Outdegree

Answer 9

For a directed network, the outdegree of a node is the number of outgoing edges from that node

Question 10

Path

Answer 10

A route from one node to another, following edges on the network. On a directed network, this routed has to follow the direction of the edge

Question 11

Weighted Network

Answer 11

A weighted network is one where every edge has a particular value or weight, attached to it

Question 12

Unweighted Network

Answer 12

An unweighted network is one where the value of every edge is the same, there is no particular weight attached to any edge

Question 13

Shortest Path

Answer 13

The shortest path possible, given the available edges, from one node to another. For a weighted network, the shortest path is the path where the sum of the weights of all edges along the path is lowest. Also called a geodisc.

Question 14

Distance

Answer 14

The number of edges in the shortest path between two nodes. For a weighted network, the sum of the weights along the shortest path.

Question 15

Diameter

Answer 15

The longest distance among the distances between all pairs of nodes in the network

Question 16

Centrality

Answer 16

Some measure for each node indicating how ‘important’ or ‘central’ that node is in the network. Different measure relate to different concepts of ‘central’ in the context of social networks, and there are many such measures available
1. Degree centrality (many paths from this node) -> influence
2. Eigenvalue centrality (high degree to high degree connection) -> more influence
3. Betweeness centrality (many short paths go through central nodes) -> control
4. Closeness centrality (has the most shortest maths) -> independence

Question 17

Degree Centrality

Answer 17

The node with the most degrees (many paths from this node) -> influence

Question 18

Betweeness centrality

Answer 18

Here the centrality of a node is measured as the number of geodiscs (shortest paths) going through a particular node.

Question 19

Eigenvalue centrality

Answer 19

high degree connection to high degree connection (influence)

Question 20

Closeness centrality

Answer 20

Has the most shortest paths (independence)

Question 21

Homophily

Answer 21

Homophily refers to the tendency of individuals to associate and bond with others who are similar to themselves in terms of characteristics like beliefs, social status, or interests.

Question 22

Preferential Attachment

Answer 22

Preferential attachment describes the process where new nodes in a network are more likely to connect with nodes that already have a high number of connections. This process contributes to the “rich-get-richer” effect, where well-connected nodes keep gaining more links.

Question 23

Triadic Closure

Answer 23

Triadic closure is the principle that if two people have a mutual friend, there’s a higher likelihood they will eventually connect directly. This principle often reinforces the density and cohesion of networks.

Question 24

Reciprocity

Answer 24

Reciprocity measures the tendency in a directed network for mutual links to occur. Essentially, if one individual connects to another, reciprocity examines how likely it is that the connection will be reciprocated.

Question 25

Assortativity

Answer 25

Assortativity in networks refers to the tendency of nodes to connect with others that are similar in certain properties (e.g., degree of connectivity, attribute values). It often indicates that similar types of nodes group together.

Question 26

Politically Charged Connections

Answer 26

These refer to links in a political network that carry a positive or negative association. Positive connections might indicate alliances, support, or cooperation, while negative connections suggest opposition, conflict, or competition.

Question 27

Community Detection in Political Networks

Answer 27

This involves identifying clusters or groups of actors (such as individuals, organizations, or states) that have stronger connections within the group than outside it. These communities often reflect shared political agendas, interests, or ideologies and can reveal underlying structures in political networks.

Question 28

Modularity in Networks

Answer 28

Modularity measures the strength of a network’s community structure. A high modularity score means the communities are well-defined, with dense connections within groups and sparse connections between them, indicating a meaningful community division.

Question 29

Political Independence Index

Answer 29

This is a measure of a node’s (actor’s) degree of autonomy within a political network, calculated by subtracting the count of negative connections from positive ones. Connections are weighted to reflect their proximity to the actor, meaning closer connections contribute more than those further away.

Question 30

Centralisation

Answer 30

An overall measure of the level of concentration or centralization in the network

Question 31

One mode network

Answer 31

A network where all nodes are of the same type.

Question 32

Two mode network

Answer 32

A network where there are two types of nodes. Often used to denote membership, e.g. some nodes are parties and some politicians.

Question 33

Bipartite Network

Answer 33

A network where it is possible to split all the nodes in two groups in such a way that all edges connect a node from one group to a node from the other group. Many two-mode networks are bipartite networks (e.g. when edges indicate membership), but not all are and not all bipartite networks are two-mode networks.

Question 34

Terrorism

Answer 34

Form of political violence to prompt a particular response in a given population.

Political strategy to instil terror in a population to get across a particular ideology.

Physical action against civilian population to acheive a goal

Question 35

Small world networks

Answer 35

A small-world network is a type of network characterized by high local clustering and short average path lengths between nodes.

Question 36

Circle Networks

Answer 36

A circle network is a simple structure in which each node (or actor) is connected to exactly two others, forming a closed loop or ring.

Question 37

Random network

Answer 37

Random networks are networks where connections between nodes (or actors) are established at random, rather than following any specific pattern or clustering tendency.

Question 38

Village Networks

Answer 38

Village networks refer to social networks within rural or village communities, characterized by close-knit, overlapping relationships shaped by shared geography, kinship, and economic interdependence

Question 39

Opinion Leader Networks

Answer 39

Opinion leader networks are social networks centered around individuals, known as opinion leaders, who have a significant influence on the beliefs, attitudes, and behaviors of others within a community or social group.

Question 40

Cascade Diffusion Theory or Threshold Model of Collective Behaviour

Answer 40

This model describes a diffusion process in which individuals decide whether to adopt a behavior based on the number of people around them who have already adopted it.

Question 41

K-core

Answer 41

In social network analysis, a k-core is a subgraph (a smaller section of the overall network) where every node (actor) is connected to at least k other nodes within that subgraph. In simpler terms, it’s a group of people who are all well-connected to each other, and each person in the group has at least k connections with other people in the group.