Social Network Analysis

12th February, in social computing class, we learned about Social Network Analysis. Do you know what is about Social Network Analysis?. I ever told you little about Social Network Analysis in ‘Introduce to Social Computing’ post;

Social Network Theory or Social Network Analysis :  Social network theory considers social relationships in terms of nodes and ties. Nodes are the individual persons within the networks, and ties are the relationships between them. ” The network can also be used to determine the social capital of individual actors. These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines.”[1]

So, in this post, i will give you more details about Social Network Analysis.

Social network analysis (SNA)

is the methodical analysis of social network. It considers social relationships in terms of network theory, consisting of nodes and ties.
Nodes, represents individual actors within the network.
Ties, represents relationships between the individuals, such as friendship, kinship, organizational position, sexual relationship

These networks are often show in a social network diagram or social graph, where nodes are represented as points and ties are represented as lines.[2]

600px-Graph_betweenness.svg-3[3]

Network Topology

A network topology is how devices are connected over a network. There are six different common topologies as you can see below.

network-topology[4]

Bus Topology : All devices on the Bus Topology are connected using a single cable.
Ring Topology : It is a lot more complex, connected in a circle. There is no end on a Ring Topology.
Star Topology : The Star Topology works by connecting each node to a central device.
Extended Star Topology : It is a bit more advanced. Instead of connecting all devices to a central unit, it have sub-central devices added to the mix.
Hierarchical Topology : It is much like the Star Topology, except that it doesn’t have a central node. It look like Tree Topology.
Mesh Topology : The Full-Mesh Topology connects every single node together.

K-Means Algorithm

k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.”[5]

For more details, see related link below. Thank for visited my post.

Related link

[1] http://www.istheory.yorku.ca/socialnetworktheory.htm

[2] http://en.wikipedia.org/wiki/Social_network_analysis

[3] http://en.wikipedia.org/wiki/File:Graph_betweenness.svg

[4] http://learn-networking.com/network-design/a-guide-to-network-topology

[5] http://en.wikipedia.org/wiki/K-means_clustering

http://www.cs.uvm.edu/~xwu/kdd/Slides/Kmeans-ICDM06.pdf

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