Social Network Analysis

To see the publication on “Growing Social Networks”, click here.

Where people and their relationships form the backbone of an organization, social network analysis is focused on uncovering the patterning of people’s interaction. This method, also referred to as SNA, will allow the organization to identify vulnerable or strong points in the organization, as well as insight in best ways to launch or improve communities of practice. For instance, visualizing a community network can help identifying experts and leaders, as well as isolated participants or ‘cliques’ in the community. SNA will create useful patterns of information of the way people relate, which is many times in sharp contrast to the formal organization to which they belong. Network analysis creates a framework in which mathematical measurement of structures and systems can exists – which could be almost impossible to describe without relational concepts; by doing so it takes the method away from metaphors or intuition which are described in social or personal relationships. Some of the benefits of social network analysis for communities include:

  • Identify thought leaders
  • Map the impact of communities on relationships
  • Provide high level information on driving future community membership
  • Group expertise for innovation
  • Visualize isolated or non-participative groups

Often, social network data is collected by having people report on their own interactions (sociometric questionnaires, for example). Looking at social network analysis for the purpose of communities of practice, a more cognitive approach can be taken by asking respondents to tell about their relationships with other members of the community.

An example of a simplified analysis is the result of such questionnaire, where everyone in a (mostly) virtual global community was asked to respond the following question: Who has ever taken the initiative to personally speak to someone else on the phone, or meet face to face?

The results were processed and the outcome graphically represented (darker color implies longer community membership):


In this particular example, it became apparent that person 20, together with 21, was one of the most central figures in the network. She had been longest in the community and was considered to be an expert by most participants.

Relatively new people (for example 32, 22 and 8) to the group found, which is depicted as isolated nodes in the graph, having barriers to participate – which inevitably resulted in barriers to contact more experienced network members.

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