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"Connectivism: Learning as Network-Creation," by George Siemens, elearnspace, 10 August 2005, http://www.elearnspace.org/Articles/networks.doc (from ZenPundit).

An interesting view of thinking and instruction

Many attributes of networks from sociology and physics transfer easily to the concept of networked learning.

Small world effect is based on research carried on my Stanley Milgram. He discovered that most nodes within a network are connected by a fairly short path. Information flow from one domain of a well developed network to another generally requires a small number of “hops”. A learning network has similar short paths between information elements.

Weak ties are links or bridges that allow short connections between information. The concept of weak ties expresses the understanding that much of our information comes from weak connections to networks other than our own. Our personal network is populated with nodes that are integrated with (or at least similar to) our own. For substantial innovation to occur, we often rely on loose/weak connections to other less familiar networks. These connections provide a view into modes and manners of thought which differ significantly from our own.

Scale Free networks are detailed by Ravid and Rafeali (2004):

“In Scale Free networks the distribution of different network parameters act in an exponential fashion. The most interesting and most measured exponentially distributed parameter is the distribution of connections from each node outwards (Out Degree). This uneven distribution means that in these networks some of the members are connected to a lesser degree and some of the members are connected to greater degree, which is how they hold a senior position in the network (Goh, et al., 2002). Networks of this type are relatively resilient, but are not at all immune to attack. In other words, a random removal of network members (a crash) will not hurt its stability, but a directed removal of key points — hubs — will cause the network to quickly collapse. On Scale Free networks, the distribution of density or congestion is constant and is not dependent on the exponential coefficient of the distribution of the number of connections (Jeong, 2003)”.