Node: in mathematical terms, each object in a social network graph is called a node. In sociological terms, each node can also be called an actor.
Edge: in mathematical terms, the line linking more than one node is called an edge.
Tie: the edge between two nodes can also be called a tie. Ties can be directed or undirected. Most of the social network analysis case studies we have undertaken feature undirected ties; the relationship between the two actors is the same in both directions. Directed ties exhibit ‘directionality’, meaning that the relationship between the individuals is uneven. Grantor and beneficiary analysis involves directed ties, because there is the gift is moving from the grantor to the beneficiary.
Sociogram: graphs in SNA are known as sociograms.
Dyad: A pair of two actors in a sociogram.
Triad: A group of three actors in a sociogram. The theory of dyads and triads predates social network analysis, going back to the work of sociologist Georg Simmel (1858-1918).
Clique: three or more actors who are all connected directly to each other
Centrality: ‘importance’ (often suggesting influence, leadership, or flow of information) of a person in a sociogram (social network graph)
Degree centrality: how many people a single actor is connected to; number of contacts a person has in his/her network
Eigenvector centrality: a refinement of degree centrality which weighs the importance of the contacts in one’s network according to their degrees. Tells us not just how many people an actor is connected to, but also how many important people an actor was connected to.
Betweenness centrality: a form of centrality that focuses on the place of an actor within a network structure. This is calculated by looking at how many times an actor sits on the shortest path (called the ‘geodesic’) linking two other actors. A characteristic example of a person with high betweenness centrality is William del Bois, chancellor (d. 1232). In this sociogram of actors witnessing together more than twenty times, he occupies a pivotal position.
Closeness centrality: a form of centrality that emphasizes the independence of an actor. Being close to many other actors means that a particular actor will not have to rely on intermediaries, and is better placed to be a mobilizing force. In that sense, closeness is something of a corollary to betweenness. Closeness centrality is measured using a concept called distance. Distance in a sociogram is the length of the shortest path between two nodes.
Ego-network: a network defined as all the contacts of a single actor. In other words, the network is composed only of the defining actor (‘ego’) and those to whom he/she is directly connected (‘alters’). The ego-network emphasises the extent to which those alters are connected also to each other. ‘How many of ego’s friends know each other?’
Ego-network size: because the ego-network is defined as all the people connected to ego, the ‘size’ of the ego-network (number of ‘alters’) will always be the same as ego’s degree centrality.
Ego-network density: perhaps best thought of as ‘connectedness’, or the extent to which all the actors in the network are interconnected. Density is expressed as a fraction or a percentage, in terms of how many potential ties are actually present. An ego-network density of 1 (or 100%) means that all the people in the graph are connected to each other. A low density means that most of the contacts of ego are not also connected to each other, or that many of ego’s friends and acquaintances do not know each other.