Is your community a small world?

Every community should be like a small world: a number of subgroups connected with each other through bridge builders Every community has its own interaction patterns (structure) With the right data, you can calculate if your community is a small world.

Is your community a small world?

I had lunch with a couple of people at a co-working place. One of the guys mentioned that Galway is a small city, everyone around him is just one degree away: You know my colleague, you are the neighbor of my cousin and so on he went. Unbeknown to us, we were part of his small world. While we were strangers, we had connections. These connections were not direct, as we had not talked with each other before we sat down at the communal lunch table. But we had indirect connections. Communities try to recreate this small world effect with a group of strangers.

While this post specifically addresses community builders, the idea of a small world network is relevant to job seekers, managers, and your personal life.

Key points

  • Every community should be like a small world: a number of subgroups connected with each other through bridge builders
  • Every community has its own interaction patterns (structure)
  • With the right data, you can calculate if your community is a small world.

What is a small world?

A small world is a technical term to describe a specific type of community (network) structure. Network or community structure is the relationships and connections people have. These connections form an interaction pattern. This structure can be like a chain, similar to the kids’ game telephone, like a ring, or any other shape you can imagine.

In some communities, information travels slowly, it kind of gets stuck in a part of the community and can't break free and reach another subgroup. In other communities, information travels at a very high speed, quickly reaching everyone. How quickly information travels in a community, depends on its structure. The speed at which information travels is important, for example, to announce events.


In a small-world network, information such as resources, tips, event information, knowledge, travel at an ideal speed. This is because it has two features:

  1. Clusters of people (subgroups): A small group of people who talk a lot with each other
  2. Bridges between these subgroups: People who are part of several clusters.

Clusters are important for knowledge sharing. It is thanks to this high level of interaction within a cluster that people become familiar with each other. This is important as it establishes trust. But if there are only clusters, there would be no innovation, no cross-pollination. Just the same thing. Over and over again.


People who build bridges by being part of different clusters help knowledge be accessible across the community. In slightly technical terms, they make the community less big by decreasing the distance between members.

The concept of small-world began with Stanley Millgram's experiment. A more modern version of this is Kevin Bacon's 6 degrees of separation. The idea is the same: The world is smaller than it is if we look for bridge builders connecting seemingly disconnected groups.

A small-world network and a random network. Adapted from ...

Why the small-world idea is important for community management

While the purpose of every community is different, communities have a vibe of togetherness. People become members of these communities because they share the community's vision or values, or want to access the resources available to community members. A community is like this living, breathing thing that - ideally - takes a life of its own and becomes something bigger than its individual members.


Creating this living community can only work if the community grows beyond the vision, expertise, and time of those who have initial created the community. A community that is sustained solely through the activities of the community founders will die, once these founders step aside. If you would draw this community, you'll see a classic star network, with the founding team at the center. You remove the center and all breaks apart.: The community is dead.

A classical star network


What you want to achieve is to create a small-word structure: Allow for clusters to exists by letting members create groups around similar topics and interests. But at the same time, offer and stimulate opportunities for interactions outside these specialist subgroups.

How to analyze and improve the structure of your community?

Put briefly, analyzing community structure is nothing more than looking at who is talking with whom, creating a network graph, and running a number of tests. The difficult, or time-consuming part is, collecting the data. Even if the community is online, getting the data in the right format (edgelist or matrix) might take some time. But once you have that, the analysis isn't too complicated. The harder part is making sense of it: What does it mean? How to best measure what you are interested in?


The final step is experimentation: What can you do to shape the community structure? Can you reach out to specific people? What event themes and activities will help you develop a better community structure?

Are you currently managing a community or a team? What data do you use to answer the question Is this community working for its members? How commune or divided are we? I'm working on a workshop to address these questions and would love to hear from you.

Leave a comment

Sources

  • de-Marcos, L., García-López, E., García-Cabot, A., Medina-Merodio, J.-A., Domínguez, A., Martínez-Herráiz, J.-J., & Diez-Folledo, T. (2016). Social network analysis of a gamified e-learning course: Small-world phenomenon and network metrics as predictors of academic performance. Computers in Human Behavior, 60, 312–321. https://doi.org/10.1016/j.chb.2016.02.052
  • Singh, P. V. (2010). The small-world effect: The influence of macro-level properties of developer collaboration networks on open-source project success. ACM Transactions on Software Engineering and Methodology, 20(2), 1–27. https://doi.org/10.1145/1824760.1824763
  • Zhang, Y., Li, X., Aziz-alaoui, M. A., Bertelle, C., Guan, J., & Zhou, S. (2017). Knowledge diffusion in complex networks: Knowledge diffusion in complex networks. Concurrency and Computation: Practice and Experience, 29(3), e3791. https://doi.org/10.1002/cpe.3791

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