Sunday, July 24, 2011

Social Networks at Work

The theme for the INFORMS Blog Challenge for July is "O.R. and Social Networking". I suspect that most posts will deal either with how O.R. tools can be applied to social networks (algorithms that recommend new "friends", managing network traffic, ...) or how social networks can benefit O.R. people. I hope to take a slightly different tack here. Operations research, management science and analytics propose to help people make better decisions and help systems operate more smoothly, and I think we have something to offer in better understanding and managing the role of social networks in the workplace.

Let me start by disclaiming any originality of the following ideas. Researchers in organizational behavior have already taken note of social networks in and between organizations. The central notion is that individual workers and groups of workers gain productivity by leveraging contacts in other units of the same organization or in other, separate organizations. Often those are direct contacts, but sometimes not. (To be concrete, if we picture a social network as a graph with actors or groups of actors as nodes and relationships as edges, direct contacts are nodes adjacent to a given node. Indirect contacts are nodes connected to a given node by a path of length greater than one.) There are various reasons why an organization might be concerned about social networks within it and between it and other organizations:
  • If workers become more productive by exploiting links, the organization might benefit from fostering the development of such links.
  • A social network within an organization may improve intra-organizational communication.
  • Conversely, a social network within an organization might contribute to the development of cliques and cause a rise in "political" behaviors.
  • Contacts with members of allied organizations might improve the relationship between the organizations (for instance, helping coordinate a supply chain).
  • Contacts with members of competing organizations might foster cooperation on some issues, but also might lead to leakage of proprietary information.
  • Individuals with many valuable connections (nodes with high degree, either weighted or unweighted) may be of particular value to the organization, warranting extra effort to retain and reward them.
  • Unmanaged development of social networks, both within the organization and leading to the outside, might result in the workforce being divided into "ins" (highly connected individuals) and "outs" (nodes with low degree). To the extent that the social network improves performance, the "outs" may be at a disadvantage in career development. This can undermine mentoring and retention efforts, and may be a particular concern for minorities and non-domestic workers.
To manage social networks, organizations need to be able to quantify them. This means:
  • defining what constitutes a network;
  • mapping the network;
  • finding a way to quantify things such as influence level and value to productivity (which may not be symmetric, meaning the network is directional);
  • identifying options for creating or manipulating networks (decision variables);
  • estimating the cost and potential impact of each option;
  • assessing risks of various options (including allowing networks to grow unmanaged); and
  • finding an optimal program of network construction/management.
(Being an optimization guy, I had to squeeze that last item in.) The measurement aspects, including mining existing data (emails, phone records, reports) to help identify existing networks, sound like analytics; the mapping, analysis of costs and impacts, and prescriptive recommendations sound like operations research. So I think we have something to contribute here.

And the great thing about being a blog author is that I'm not required to come up with any solutions (or even brilliant insights). :-)


  1. Holy Flying Spaghetti Monster...

    we are [not!] running out of approaches [or: terms used] to model and analyze social networks!

    - Data Mining
    - Graph Mining
    - Machine Learning
    - Knowledge Discovery in Databases
    - etc.pp.

    Btw, whoever attends SIGKDD 2011 might be interested in:

  2. Small World Graph theory seems amazingly applicable to this topic:

  3. Social networks probably start off as small world graphs. One possible difference is that direct links between clusters appear to come into being as the network evolves, so that it may possibly lose its "small worldness" over time.


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