| Node |
Nodes represent actors within a network. |
A node represents the individual authors (or research institutions) within the co-authorship networks. |
| Edge |
Edges represent ties or relations within a network. |
The edges in the network represent the co-authorship of different authors. All authors in the network that have published together in the covered timeframe are connected through an edge. |
| Betweenness Centrality |
The betweenness centrality score is a measure of how often a node lies on the shortest path between nodes in the network[15]. Nodes with a high betweenness centrality often connect components of a network that would be disconnected if the node is removed. |
A high betweenness centrality indicates that an author is frequently identified if you want to connect other authors in the co-authorship network with one another, and he/she lies "between" them as an intermediary. |
| Average degree |
The degree states the quantity of direct neighbours of a node in a network. |
Here, the degree states the sum of co-authors the respective author has published with in the covered timeframe. The average degree is calculated separately for each disease network. |
| (Giant) Component |
Components of a graph are sub-graphs that are connected within but disconnected between sub-graphs. The term "giant component" is used for the sub-graph with the most nodes in the network [16]. |
Different components of the co-authorship network contain authors that are connected with one another through joint publications. They have not published with authors in the other components of the network within the covered timeframe and are therefore not connected in the network. |
| Graph density |
Graph density is a measurement of how close the network is to being complete. If all nodes of a network are connected to each other, the graph density equals one [17]. |
For research networks, the graph density can be used as an indicator of how many possibilities there are for further collaborations between authors. |
