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The aim of Social Network Analysis is to visualise and understand the relationships between people and then act upon these findings. People within the network are referred to as nodes, relationships between people are referred to as ties, while information about the node are node attributes.
Social Network Analysis provides a visualise map of where crucial information and knowledge is flowing within the business. For example, suppose you want to analyse who is talking to who and how frequently this collaboration happens in a research project. Using a social network analysis survey to ask the question “Who do you collaborate with?” across the sample of users will provide you with the data to create the network map. The questions typically use a Likert Scale to gauge how often collaboration occurs:
1. Very Frequently
5. Very Rarely
The data collected from all respondents is then transferred to a matrix. Where people are labelled as nodes and the strength of the relationship between nodes is labelled 1 to 6. This marix can then be analysed using UCINET or NetDraw. Both software programs are typically used for Social Network Analysis and both allow statistical analysis. However, UCINET itself does not visualise the network, NetDraw is bundled within UCINET and provides that capability.
Social Network Analysis is interested in the whole of the network and not just the individual’s ties and links. Taking this holistic view or Systems Engineering approach, allows the structure of the ties and how they interact in terms of strengths (derived from the Likert Scale) to be analysed.
Once you have your social network analysis data then it can be used to visualise the social relationships and identify collaborative groups, cliques of people, areas of poor collaboration and personal networks (ego networks). This information is very powerful and can help to identify where collaboration, knowledge management research and Information Management can help. Powerful knowledge sharing people often bridge networks together and these are called knowledge brokers. When working in teams you would assume that the knowledge broker would be the team leader, but you may also discover that a team members has a wider collaborative network.