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Table 1 Social network analysis (SNA) terms and their implications for knowledge translation

From: How the study of networks informs knowledge translation and implementation: a scoping review

SNA term (frequency count) Definition Implication for KT
Network An interconnected group of actors (e.g., people, organizations) [7] Provides the social context within which KT occurs
Actor A point (node) in a network that represents an individual, organization or entity connected to other actors (through ties) [7] Represents the people, teams, or organizations involved in KT processes
Tie (2) The relations or connections among actors in the network [79] Represents the interactions, collaborations, or relationships involved in KT Measures one- versus two-way communication, advice seeking, collaboration, etc. [7]
Dyad Pairwise relations between actors [7] Represents one of three levels of analysis for social network data (the others being individual node-level and whole network level) [7]
 Whole network centralization (3) Extent to which interconnections are unequal across the network [21] (i.e., concentrated around one or more central individuals) [7] Thought to enhance ease of knowledge sharing and to promote standard practices of existing protocols [80]. Decentralization may support new innovations, but lead to mixed messaging and decreased clarity because of multiple information sources [72]
 Degree centrality (3) # of direct ties (connections) of an actor Seen as an indicator of visibility [81], prestige [39] or power [79] resulting from lots of direct contact to many others
 Indegree centrality (10) # of individuals who send (identify) ties to an actor Considered an index of importance [28] power or influence [40]
 Outdegree centrality (5) # of direct ties an actor sends (identifies) to others [33] Used to quantify access to resources through colleagues, exposure to evidence and others’ practices; positively associated with EIP use [33]
 Betweenness centrality (4) Extent to which an individual is tied/connected to others who are not connected themselves [40] Used as a proxy for control of KT processes [39]; high values reflect a favorable position (e.g. brokering potential) [40] for information flow or power [79]
 Flow betweenness centrality (3) How involved an actor is in all of the paths or routes between all other actors (not just those representing the shortest paths) [79] Used to determine contributions of individuals toward team decision-making; provides insights into structural hierarchy [33] Used as a proxy for ease of bypassing the core individuals in the network [39, 79]
 Closeness centrality (2) Proportion of actors that can be reached in one or more steps [79] Proxy for degree of access to information [39] or efficiency in communicating with the network (relative reach) [7]
 Bonacich centrality (1) Extent to which an actor is tied to others, weighted according to the centrality (e.g., popularity, importance) of those to whom the actor is tied/connected [79] Proxy for power or hierarchy within a network; may help to identify network fragmentation/brokering opportunities [14]
 Hubs and authorities centrality (1) The structural prominence of individuals within a core-periphery structured network [32] Proxy for importance [32]
Tie characteristics
 Tie strength (7) Value associated with a tie/connection, e.g., frequency of contact, emotional intensity, duration of connection, etc. [7] Weak ties thought to increase access to new information/opportunities; strong ties seen as required for innovation implementation [82]
 Tie homophily (includes external-internal or EI index) (13) Similarity of connected actors/nodes on a given attribute [7] Similarities among people create conditions for social contagion (individuals may be more likely to modify their behaviors/attitudes to match those around them) [67, 83]
 Tie hierarchy (1) Connections between actors dissimilar in their status (e.g., according to profession, leadership or power position) [7] Hierarchy may be a barrier to innovation adoption (e.g., lack of interest from above/resistance from below [29]
 Tie reciprocity (8) The extent to which directional ties to actors are reciprocated (i.e., are bi-directional) [79] Reciprocity may reflect greater stability or equality (versus hierarchy) [79]
 Euclidian distance (1) A measure of the dissimilarity between the tie patterns of each pair of actors in the network [79] Can be used to identify key people by their dissimilarity to others (e.g., who has the most research productivity relative to their connected peers) [28] (as a proxy of influence)
 Whole network density (8) An index of the proportion of existing ties relative to all possible ties in a network [79] Proxy for efficiency of information flow [79], solidarity [84], or cohesiveness within a network [21]
 Ego network density (2)
 Components/isolates (3) Portions of the network that contain actors connected to one another, but disconnected from actors of other subgroups [79] Subgroups and isolates can be targeted to increase connectedness, share information, or mobilize action
 Cliques (1) Maximum # of actors who share all possible connections among themselves [79] Can describe paths for fostering awareness and adoption of interventions [23]
 Clusters (4) Dense sets of connections in a network [79] Identifying attributes that influence clustering helps understand KT-related behaviors, such as information seeking (e.g., experts; same department) [36, 41]
Network roles and positions
 Brokers (1) Actors holding bridging positions in a network (i.e., play a role in connecting subgroups) [79] Can leverage brokers’ positions for efficient KT by leveraging their tie paths/connectedness [36, 37, 79]
 Coreness/Core-periphery index (2) The core of a network represents the maximally dense area of connections, whereas the periphery represents (to the maximum extent possible), the set of nodes without connections within their group [79] Power/influence at the core [39]. The most active EIP practitioners may be found at periphery [32]
 Structural equivalence (2) When two actors/nodes have the same relationships to all other nodes in the network—they can be substituted without altering the network [79] These positions may generate social pressure within a network [24, 25]
 Structural holes/constraint (ego network) (2) Structural holes: absent ties in a network that limit exchange between actors; constraint: degree to which an actor is tied to others who are themselves connected [79] Inequality among actors can be identified and targeted through KT interventions; may have implications for EIP adoption [31] (e.g., many ties may restrict one’s actions/capacity) [79]
Transitivity/network closure (i.e., network structure related to triads)
 Alternating k-stars (4) The tendency of actors to create ties [29] Used as an indicator of hubs within a network [37] or the tendency to share/exchange knowledge [29]
 Alternating k-triangles/transitive triads and/or non-closure structures (5) The extent to which sets of 3 actors form patterns of connections that create larger “clumps” within the network [29, 79] Assesses tendency to build relationships outside of one’s local group—access to new knowledge [29]
 Cyclic closure (1) The tendency for transitive triads (sets of three actors in which two ties exist) to lead to reciprocal ties within that triad [27] Cyclic closure thought to reflect non- hierarchical knowledge exchange, which is more effortful to maintain and therefore less likely to be seen in knowledge sharing networks [27]
 Alternating independent two-paths (2) Assesses the conditions required for transitivity (i.e., ties that form between each pair of actors in a set of three actors) [29] Can determine the extent to which actors tend to build small, closed, non-hierarchical connections that limit broader access to new information [29]
  1. SNA social network analysis, KT knowledge translation