Scientometrics, Knowledge Management, and Social Network Analysis

The following five practices can be used to overcome the barriers that undermine many organizations’ ability to identify and execute innovation opportunities:

1. Create a network-centric ability to sense and respond to opportunities. Building awareness of who knows what in a network is critical for people to tap the right expertise at the right time.
2. Develop an ability to rapidly test and refine an opportunity. Mapping decision-making networks so that emerging opportunity can be tried rapidly.
3. Work through people in specific network positions. Engage people who are information brokers who can reach out to other key connectors in the network. The idea is to bring diversity of people to work on the new idea as it  is critical to its quality and to the ease of implementation (i.e. preventing the idea to be developed in isolation).
4. Leverage energy. Mapping enthusiasm in networks to indicate who makes them feel energized provides a powerful indicator of where creativity and innovation are occurring.
5. Ensure that organizational context supports collaboration. Simply introducing a collaborative technology, tweaking incentives, or advocating cultural programs to boost collaboration is insufficient. What is also required is the alignment of unique aspects of formal organization design, control systems, technology, and human resource practices. Specific cultural values and leadership can also have striking effects of collaboration.

Source: Chapter 3 of Driving Results Through Social Networks: How Top Organizations Leverage Networks for Performance and Growth by Rob Cross

What can be expected from doing a social network analysis within an organization? What can be identified by analyzing the social networks?  According to The Leadership Alliance, here are some:

• Bottlenecks in key business processes;
• What would happen to a team if key members left;
• Sources of informal influence;
• Employees who connect to the far reaches of the organisation;
• A good candidate for managing a key department or a new department;
• Boundary spanners between contiguous network structures i.e. ’silos of expertise’;
• Degree of employee collaboration and interactivity;
• “High Potentials”; and
• Opinion leaders

Social Networks Analysis can also provide indicators for monitoring:

• The informal leadership of specific groups;
• Influencers on products/processes/services;
• Product/process experts (‘hubs’ and ‘authorities’);
• Fragmentation and ‘structural holes’; and
• The ‘reach’ of people (their influence)

Note: A social network analysis can be effectively done for a network group size between 25-300 (according to Andrew Parker – the co-author of The Hidden Power of Social Networks). If we do it for a network group with size of more than 300, it can be to time consuming especially for the person who have big personal network and if we do it for a network group size of less than 25, the group would have already known the result of analysis anyway.

This website provides information on, background material for and access to the statnet suite of packages for network analysis. Directions for downloading statnet can be found under Installation on the navigation bar to the left. The packages are written for the R statistical computing environment, so it runs on any computing platform that supports R. If you do not already have R installed, you will need to install it via the main R web resource-site, www.r-project.org. Instructions for installing R can also be found under Installation.

See more at its website: http://statnet.org/

I’ve seen this diagram before, but was reminded again of it through digg today. I wonder what role knowledge management plays in the network of companies shown below.

Source: zoharma

Here are some interesting SNA studies that I found:

Connecting the Dots: Tracking Two Identified Terrorists

Early in 2000, the CIA was informed of two terrorist suspects linked to al-Qaeda. Nawaf Alhazmi and Khalid Almihdhar were photographed attending a meeting of known terrorists in Malaysia. After the meeting they returned to Los Angeles, where they had already set up residence in late 1999. What do you do with these suspects? Arrest or deport them immediately? No, we need to use them to discover more of the al-Qaeda network.

527 Committee Donors (via ire)

In the 2004 presidential election “huge donations of a handful of wealthy liberals named Linda Pritzker, Stephen L. Bing, Peter B. Lewis and George Soros could determine the outcome. Together, they have given more than $26 million to help finance the most extensive get-out-the vote operation in history, the goal of which is to make John F. Kerry president.” These donations were made to 527 organizations. “Named after a section of the tax code, the 527 groups are doing much of the advertising and field work traditionally left to party organizations.” Included with this story is a diagram displaying contributions to Democratic 527s and a list of the biggest donors to these groups.

And finally, 17 ways to visualize the twitter universe via flowingdata.

I found some useful resources that teach network analysis on the web, here they are:

• Steve Borgatti’s course
• Methods for Social Network Analysis by Tom A.B. Snijders
• Social Network Analysis Graduate Course by Barry Wellman
• Organizational Network Analysis by Rob Cross
• Program on Networked Governance (Compilation by JFK School of Government)
• Compilation by KM4DEV (Knowledge Management for Development Site)
  

It’s simply a way to represent relationships using nodes and ties. The nodes represent the actors, and the ties representing the type and strength of the relationships among the actors.

Network analysis has been growing to the terms where there are businesses concentrating on providing network analysis, such as this one.

Social Network Analysis can be used in knowledge management initiative, specifically if you are in the cartographic school of knowledge management. SNA can be used to know who are the key people in the organization and how important are them for the organization.

Published in: Proceedings International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET
Authors: Lixin, Chen and Zeyuan, Liu
Affiliation: Dalian University of Technology, the 21st Century Development Research Center, Dalian 116024, the People’s Republic of China

Chen and Liu (2006) examined the international collaboration in the field of mechanics among authors in different countries using the social network analysis perspective. 168,689 mechanics-related articles in 106 journals from 1945 to 2003 were collected from the Science Citation Index-Expanded Database. The authors of the articles were from 150 countries, but only the collaborations among 30 most productive countries were analysed, which was ranked based on the number of the first-authors.

Based on the publication data of the 30 most productive countries, a non-symmetrical collaboration matrix was constructed with the rows representing first-author’s countries and the columns representing non first-author’s countries. The diagonal of the collaboration matrix was not given any value.

Chen and Liu identified the 6 most productive countries, namely USA, UK, Japan, France, Germany and China. These 6 countries accounted for 66.8% of the 88.891 publications from the top 30 countries, and 58.37% of 18,660 collaborations among the 30 countries. There were 666 different pairs of collaborations among the 900 possible collaborations. This means that the density of the network is 0.74.

The degree centrality, which is the number of direct connections each node has, was also identified. Only the top 6 countries had a degree centrality of at least 7 led by USA with 26, UK with 18, Germany with 16, France with 14, and both China and Japan with 7 degree centralities.

Finally, Chen and Liu divided the 30 countries into 4 regions, namely Europe, North America, Asia, and other. They found that European countries had a tendency to collaborate with other European countries, which accounted for more than half of the European countries’ collaborations and 26.7% of total collaborations. The percentage of collaborations by European countries was the highest compared to other regions’ collaborations. However, they were not the most productive region; North America was the highest with 39% (35% by USA).

Chen and Liu finally concluded that USA was the most important node in the network and European countries led by United Kingdom, Germany, and French played important roles in the international collaboration in the field of mechanics.