Networked Nonprofits and Social Media

A good compliation

Useful cheat sheet to adapt for trainings. 

This 12 minute video is well worth watching.  It gives a historical overview of our world moving from hierachical ways of working, thinking, and doing to that of networks.     Manuel Lima illustrates his TED talk with animated sketches.  

Key takeaway: (minute 9:59:)

The need for a networked nonprofit.  Everything is connected and interdepends.  Not just a specialist in one area, but need to learn from other areas. 

Make that mental shift - because of immense benefits.

Curated by Beth Kanter

http://www.bethkanter.org

An explanation about social network analysis and some guidelines for intrepretation.

So the most important thing when reading a social graph is to find out what relationships are being represented by the edges. This is even more important than what the vertices represent, because for SNA, the entities represented by the vertices will usually be people. 99% of the graph metrics out there depend heavily on the edges, so if the edge relationships change, the metrics will also change.

For example, the simplest graph metric is the degree centrality, and it measures how many connections a vertex has. For example, there are seven black edges connecting to me on the friendship graph (figure 2a), so I have seven friends. But there are only five red edges connecting to me, so I have five colleagues. My degree centrality on the beer buddy graph (figure 2c) is three, so I only have three beer buddies. Degree centrality can be computed for all users in the graph, for example, Ryan's degree centrality on the badminton pal graph (figure 2d) is two.

The interpretation of the graph metric also depends on the edge relationship. So, you cannot say anything about how many colleagues I have based on the friendship graph (figure 2a), because the colleague relationship is not being represented in the friendship graph. Even if you assume that everyone I've worked with are my friends; using just the friendship graph, then number of colleagues I have can still be anywhere from zero to seven. Therefore, do not try to make any inference or conclusion based on a graph about any relationship that is not explicitly represented by the edges. If you do that, you might as well just flip a coin or make a random guess.

The points here can be used for "networked mindset" CWRF assessment.

Network Weaving is introducing people in your network for mutual benefit.  This article provides some practical advice about how to make introductions. 

-Don't make cold introductions - give your contacts a heads up, acknowledge that you are asking something of them.

_Tell them why you want to know each other, provide context.  This avoid ackward moments.

Good definition of what strong vs weak tie is

From an emotional standpoint, this is great. It’s fun to do things in groups wjth people with whom you have a lot in common. But from an informational standpoint, Granovetter argues this interconnectedness is limiting because the same information recycles through your local network of like-minded friends. If a close friend knows about a job opportunity, you probably already know about it. Strong ties usually introduce redundancy in knowledge and activities and friend sets.

In contrast, weak ties usually sit outside of the inner circle. You’re not necessarily going to introduce a looser connection to all of your other friends. Thus, there’s a greater likelihood a weak tie will be exposed to new information or a job opportunity. This is the crux of Granovetter’s argument: Weak ties can uniquely serve as bridges to other worlds and thus can pass on information or opportunities you have not heard about. We would stress that it’s not that weak ties per se find you jobs; it’s that weak ties are likely to be exposed to information or job listings you haven’t seen. Weak ties in and of themselves are not especially valuable; what is valuable is the breadth and reach of the network.