Analyzing Social Media Networks with NodeXL: Insights from a Connected World (Derek Hansen, Ben Shneiderman and Marc A. Smith)

A guide on how to use the NodeXL plugin for Microsoft Excel for analysing electronic social networks.  I have used the tool on my email traffic and more extensively on our internal Saint-Gobain social network to analyse who are the key individuals, who are the bridges between various communities, which areas need more support etc.

A very useful tool and the book is a great guide with real world examples of how each analysis technique has been used to illuminate a situation in an organisation.



Locn. 299-300 Social media tools cultivate the internal discussions that improve quality, lower costs, and enable the creation of customer and partner communities that offer new opportunities for coordination, marketing, advertising, and customer support.
Locn. 308-10 The Gartner Group reported that social network analysis would prove to be a strategic advantage for a corporation, calling it an “untapped information asset.” 
 Locn. 335-36 Business leaders and analysts can study enterprise social networks to improve the performance of organizations by identifying key contributors, locating gaps or disconnections across the organization, and discovering important documents and other digital objects.
 Locn. 778-82 Ostrom found that successful communities had clearly defined boundaries, largely to overcome problems associated with outsiders taking advantage of internally produced or maintained resources. Boundaries are also important in that they encourage frequent, ongoing interaction among group members. This is critical because repeated interaction is perhaps the single most important factor in encouraging cooperation   
 Locn. 1034-35 Billions of texts are exchanged each day among almost 3 billion users of mobile phones and other devices.
 Locn. 1529-30 Network analysis argues that explanations about the success or failures of organizations are often to be found in the structure of relationships that limit and provide opportunities for interaction
 Locn. 1814-15 centrality measures, of which there are many, capture how “important” (central) a vertex is within the network based on some objective criteria.
 Locn. 1841-43 Density is a count of the number of relationships observed to be present in a network divided by the total number of possible relationships that could be present. It is a quantitative way to capture important sociological ideas like cohesion, solidarity, and membership.
 Locn. 1843-46  Centralization is an aggregate metric that characterizes the amount to which the network is centered on one or a few important nodes. Centralized networks have many edges that emanate from a few important vertices, whereas decentralized networks have little variation between the numbers of edges each vertex possesses.
 Locn. 1859-61  Degree centrality is a simple count of the total number of connections linked to a vertex. It can be thought of as a kind of popularity measure, but a crude one that does not recognize a difference between quantity and quality.
 Locn. 1870-73 betweenness centrality is a measure of how often a given vertex lies on the shortest path between two other vertices. This can be thought of as a kind of “bridge” score, a measure of how much removing a person would disrupt the connections between other people in the network. The idea of brokering is often captured in the measure of betweenness centrality.
 Locn. 1875-78 Burt provides compelling evidence that individuals who bridge structural holes are promoted faster than others  15. Social network analysis has many strategic applications when people in an organization can analyze their position and the position of others. Managers and leaders can recognize gaps or disconnections within organizations and devote resources to traversing the divide.
 Locn. 1881-83  Closeness centrality takes a different perspective from the other network metrics, capturing the average  distance between a vertex and every other vertex in the network.
 Locn. 1891-92  Eigenvector centrality is a more sophisticated view of centrality: a person with few connections could have a very high eigenvector centrality if those few connections were themselves very well connected.
 Locn. 1974-76 Thus weak ties proved particularly useful for finding novel information, such as information about job prospects. Because weak ties were less intense, they were also less costly to maintain in terms of time and attention. As a result, it is possible to have many weak ties but only a few strong ties.
 Locn. 2164-65 a network graph can provide an overview of the structure of the network, calling out cliques, clusters, communities, and key participants.
 Locn. 2196-99 Traditional participation statistics can provide important insights about the engagement of a community, but can say little about the connections between community members. Network analysis can help explain important social phenomena such as group formation, group cohesion, social roles, personal influence, and overall community health.
 Locn. 2880-83 Tracking aggregate graph metrics over time can determine the effectiveness of interventions on the network as a whole. For example, you would expect the total number of edges to grow, increasing the “density” of the graph, after a photo sharing activity designed to introduce people to those they don’t know.  Individual person-level metrics provide insights about a person’s 
Locn. 2884-86 For example, network graph metrics can be used to identify those people in a network who are bridge spanners or who are popular. Once identified, analysts and managers can better know who to contact or influence or bring to the table when trying to implement new programs or gain broader understanding. Locn. 3018-19  Average geodesic distance. The average of all geodesic distances. This value gives a sense of how “close” community members are from one another.
 Locn. 3926-27 Today, an estimated 1.4 billion worldwide email users send nearly 50 billion nonspam emails each day. 
 Locn. 3943-46 These maps and reports may help you realize unappreciated or forgotten relationships, or identify a past working group that could be rekindled for a current project. They can help us overcome some of our memory biases such as weighing recent events more or remembering things we’ve initiated more than those initiated by others.
 Locn. 4471-73 Despite these challenges, a number of companies have begun to create social network data that combine corporate email network data and corporate directory information, giving them a live window into their corporate communication patterns.