Definitions Flashcards
Node
An actor or entity in the network. Also known as ‘vertex’, ‘point’, ‘site’, ‘actor’, ‘entity’, etc
Dyad
A pair of two nodes
Edge
A connection between two nodes. Also known as ‘line’, ‘arc’, ‘link’, ‘tie’, ‘relationship’, ‘connection’
Network
A set of nodes and a set of edge between those nodes
Degree
The degree of a node is the number of edges connected to that node
Directed Network
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
Undirected Network
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
Indegree
For a directed network, the indegree of a node is the number of incoming edges to that node
Outdegree
For a directed network, the outdegree of a node is the number of outgoing edges from that node
Path
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
Weighted Network
A weighted network is one where every edge has a particular value or weight, attached to it
Unweighted Network
An unweighted network is one where the value of every edge is the same, there is no particular weight attached to any edge
Shortest Path
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.
Distance
The number of edges in the shortest path between two nodes. For a weighted network, the sum of the weights along the shortest path.
Diameter
The longest distance among the distances between all pairs of nodes in the network
Centrality
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
Degree Centrality
The node with the most degrees (many paths from this node) -> influence
Betweeness centrality
Here the centrality of a node is measured as the number of geodiscs (shortest paths) going through a particular node.
Eigenvalue centrality
high degree connection to high degree connection (influence)
Closeness centrality
Has the most shortest paths (independence)
Homophily
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.
Preferential Attachment
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.
Triadic Closure
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.
Reciprocity
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.
Assortativity
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.
Politically Charged Connections
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.
Community Detection in Political Networks
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.
Modularity in Networks
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.
Political Independence Index
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.
Centralisation
An overall measure of the level of concentration or centralization in the network
One mode network
A network where all nodes are of the same type.
Two mode network
A network where there are two types of nodes. Often used to denote membership, e.g. some nodes are parties and some politicians.
Bipartite Network
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.
Terrorism
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
Small world networks
A small-world network is a type of network characterized by high local clustering and short average path lengths between nodes.
Circle Networks
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.
Random network
Random networks are networks where connections between nodes (or actors) are established at random, rather than following any specific pattern or clustering tendency.
Village Networks
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
Opinion Leader Networks
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.
Cascade Diffusion Theory or Threshold Model of Collective Behaviour
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.
K-core
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.
