| Social network concept |
Definition |
Usefulness in network analysis & alliance networks |
| Average degree |
Average number of connections per node |
Provides a baseline against which to compare the degree of individual nodes and/or clusters |
| Average path length |
Average distance between any two nodes |
A reduction in the number of components along with increases in either overall clustering coefficient or network density and without a significant increase in overall average path length is an indication of increased innovation capacity (Schilling and Phelps 2007) |
| Average clustering coefficient |
Average percentage of a node’s neighbors that are connected to one another |
Global interconnectivity of a network; see connected components |
| Connected component/ component |
Subgraphs of a network supergraph that are connected to the supergraph by a single edge |
Similar to modules: help identify distinct communities in a network; Organizations in an alliance network with lower numbers of connected components and higher clustering coefficients will have greater reach towards knowledge resources within the alliance network and greater innovative output (Schilling and Phelps 2007) |
| Modularity class/ module |
Computed optimized cluster of nodes |
Similar to components: help identify distinct communities in a network (Blondel 2008) |
| Betweenness centrality |
How often a node (or node cluster if we extend the model) is found on the shortest paths between others (Freeman 1977) |
Measure of social power and represents the relative ability of a node to broker between other parts of a network; leading indicator of preferential attachment (Wasserman and Faust 1994) |
| Structural holes |
Position in a network between two or more clusters containing different information |
Individuals or organizations placed at strategic locations of centrality between otherwise disparate networks are uniquely able to broker relations between them. Such organizations have high betweenness centrality making them better able to bridge gaps in the network and regulate the flow of information (Burt 2002) |
| Preferential attachment |
New nodes prefer to join a network by attaching to the existing nodes with the highest betweenness centrality |
Fundamental to science collaboration networks (Barabási and Albert 1999) |
| Network density |
Number of actual edges in the network divided by the maximum possible number of edges. |
High levels of network density in combination with high levels of centrality offer a fairly high impact on exploration; at average technological distances, central companies in increasingly dense networks have an increasingly advantaged position to develop innovations (Gilsing et al. 2008) |
