Network analysis is a way of looking at the world that focuses on the shape and structure of collections of relationships.

In a network perspective the world is not primarily composed of individuals (“nodes”, “vertices”, “entities”). Instead, a network approach focuses on relationships between individuals (“edges”, “ties”, “connections”, “links”).

When collections of connections are analyzed, network patterns emerge. Networks have a variety of shapes and within them people occupy a variety of locations within each network. Some people are highly connected, while most people have just a few connections, for example.

Network theory provides a big collection of math that enables the measurement of these shapes and structures.

Using these measures, network analysis can identify key people in important locations in the network (for example: hubs, bridges, and islands). Network metrics allow for the network as a whole to be measured in terms of size and shape. Networks have many basic shapes and we have found six shapes to be common in internet and enterprise social media: divided, unified, fragmented, clustered, outward hub and spoke, inward hub and spoke. These shapes are created when people make individual decisions about who to reply to, link to, and like.

Divided networks are created when two groups of people talk about a controversial topic – but do not connect to people in the “other” group. Unified networks are formed by small to medium sized groups that are obscure or professional topics, conference hashtags are a good example. Fragmented networks have few connections among the people in them: these are often people talking about a brand or popular topic or event. Clusters sometimes grow among the people talking about a brand, indicating a existence of a brand “community”. Broadcast networks are formed when a prominent media person is widely repeated by many audience members, forming a hub-and-spoke pattern with the spokes pointed inward at the hub. The final pattern is the opposite, hub-and-spoke patterns with the hub linking out to a number of spokes. This pattern is generated by technical and customer support accounts like those for computer and airline companies. Additional patterns may exist, but these patterns are prominent in many social media network data sets.

When applied to external conversations, social media networks help identify the “mayor” of a hashtag or topic: these are the people at the center of the network. Network maps can be compared to the six basic types of networks to understand the nature of the topic community. We can look for examples of successful social media efforts and map those topic networks. Social media managers can contrast their topics with those of their aspirational targets and measure the difference between where they are and where they want to be.

When applied to enterprise conversations and connections, network analysis can reveal the experts who answer many people’s questions and “brokers” who bridge otherwise disconnected groups as well as the “structural holes” that show where a bridge or link is needed.

These insights can be useful in mergers, HR evaluation of group and manager performance, and identifying internal subject matter experts.

Research performed using NodeXL shows that work teams that have higher levels of internal connection (which is called “network density”) have higher levels of performance and profit. See:

The impact of intragroup social network topology on group performance: understanding intra-organizational knowledge transfer through a social capital framework
Wise, Sean Evan (2013) The impact of intragroup social network topology on group performance: understanding intra-organizational knowledge transfer through a social capital framework. PhD thesis, University of Glasgow.
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