Social Networks : Introduction to Social Network Analysis

Valdis is a management consultant and the developer of InFlow, a software based, organization network analysis methodology that maps and measures knowledge exchange, information flow, communities of practice, networks of alliances and other networks within and between organizations.

His work in organizational network analysis has been covered in major media including Discover Magazine, Business 2.0, New York Times, Wall Street Journal, USA Today, CNN, Entrepreneur, First Monday, Optimize Magazine, Training, PC, ZDNet, O'Reilly Network, Knowledge Management, Across the Board, Business Week, HR Executive, Personnel Journal, Forbes, FORTUNE,,, Release 1.0, and several major newspapers around the world.

Valdis has consulted and researched organizational networks since 1988. He works from his office in Cleveland, Ohio with a network of colleagues in the USA, Canada and Europe.

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Social network analysis (SNA) is the mapping and measuring of relationships and flows between people, groups, organizations, animals, computers or other information/knowledge processing entities. The nodes in the network are the people and groups while the links show relationships or flows between the nodes. SNA provides both a visual and a mathematical analysis of human relationships. Management consultants use this methodology with their business clients and call it Organizational Network Analysis (ONA).

A method to understand networks and their participants is to evaluate the location of actors in the network. Measuring the network location is finding the centrality of a node. These measures help determine the importance, or prominence, of a node in the network. Network location can be different than location in the hierarchy, or organizational chart.

We look at a social network, called the "Kite Network" (see above), developed by David Krackhardt, a leading researcher in social networks. Two nodes are connected if they regularly talk to each other, or interact in some way. For instance, in the network above, Andre regularly interacts with Carol, but not with Ike. Therefore Andre and Carol are connected, but there is no link drawn between Andre and Ike. This network effectively shows the distinction between the three most popular individual network measures: Degree Centrality, Betweenness Centrality, and Closeness Centrality.

Degree Centrality

Social network researchers measure network activity for a node by using the concept of degrees -- the number of direct connections a node has. In the kite network above, Diane has the most direct connections in the network, making hers the most active node in the network. She is a 'connector' or 'hub' in this network. Common wisdom in personal networks is "the more connections, the better." This is not always so. What really matters is where those connections lead to -- and how they connect the otherwise unconnected! Here Diane has connections only to others in her immediate cluster -- her clique. She connects only those who are already connected to each other.

Betweenness Centrality

While Diane has many direct ties, Heather has few direct connections - fewer than the average in the network. Yet, in may ways, she has one of the best locations in the network - she is between two important constituencies. She plays a 'broker' role in the network. The good news is that she plays a powerful role in the network, the bad news is that she is a single point of failure. Without her, Ike and Jane would be cut off from information and knowledge in Diane's cluster. A node with high betweenness has great influence over what flows in the network.

Closeness Centrality

Fernando and Garth have fewer connections than Diane, yet the pattern of their direct and indirect ties allow them to access all the nodes in the network more quickly than anyone else. They have the shortest paths to all others -- they are close to everyone else. They are in an excellent position to monitor the information flow in the network -- they have the best visibility into what is happening in the network.

Boundary Spanners

Nodes that connect their group to others usually end up with high network metrics. Boundary spanners such as Fernando, Garth, and Heather are more central than their immediate neighbors whose connections are only local, within their immediate cluster. Boundary spanners are well-positioned to be innovators, since they have access to ideas and information flowing in other clusters. They are in a position to combine different ideas and knowledge, found in various places, into new products and services.

Peripheral Players

Most people would view the nodes on the periphery of a network as not being very important. In fact, Ike and Jane receive very low centrality scores for this network. Yet, peripheral nodes are often connected to networks that are not currently mapped. Ike and Jane may be contractors or vendors that have their own network outside of the company -making them very important resources for fresh information not available inside the company!

Network Centralization

Individual network centralities provide insight into the individual's location in the network. The relationship between the centralities of all nodes can reveal much about the overall network structure.

A very centralized network is dominated by one or a few very central nodes. If these nodes are removed or damaged, the network quickly fragments into unconnected sub-networks. A highly central node can become a single point of failure. A network centralized around a well connected hub can fail abruptly if that hub is disabled or removed. Hubs are nodes with high degree and betweeness centrality.

A less centralized network has no single points of failure. It is resilient in the face of many intentional attacks or random failures -- many nodes or links can fail while allowing the remaining nodes to still reach each other over other network paths. Networks of low centralization fail gracefully.

Other Network Metrics

  1. Structural Equivalence - determine which nodes play similar roles in the network
  2. Cluster Analysis - find cliques and other densely connected clusters
  3. Structural Holes - find areas of no connection between nodes that could be used for advantage or opportunity
  4. E/I Ratio - find which groups in the network are open or closed to others
  5. Small Worlds - find node clustering, and short path lengths, that are common in networks exhibiting highly efficient small-world behavior

Software and Training in social network analysis are available from the author.

©Copyright Valdis Krebs,  2005

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