We are living in a combination of closely knitted networks, just like a spider web.We exit a network only to enter into another one. Relationships, interactions, and likeness and dis likeness can be represented with the help of graphs or networks. At home, our interactions with wife, parents, children, neighbors and relatives can be represented as nodes with edges in a graph.
At business, like in a supply chain, a supplier provides supplies to other entity that could also be a supplier to another entity. These interactions can be represented through graphs as well. Moreover, our trust, discussion, friendship attitudes at our offices can also be represented through networks and graphs.
These graphs or networks are just like our friendship interactions at face book and are named social networks as they are based on our social relationships. Identification of influential individuals/groups, group of people having common interest, people acting as bridges between two communities all can be done through social network analysis (SNA). SNA tools enable us to identify structures within graphs, important nodes, paths (communication, responsibilities, information flow) and many more.
For e.g.
Consider a protein-protein interaction(PPI) network, in a bacteria a PPI consists on about 4000 protiens. Which proteins are the most important in all others? which set of proteins are most connected (max interaction with each other making clique)? which protein causing series of actions by acting as a bridge between two communities? and many other issues can be solved through SNA tools.
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Figure. In the C. elegansinteractome, single proteins (circles) partner up (indicated by lines) to form an interconnected network that controls cell function. The inset details a small part of the network. (Image courtesy of Marc Vidal)[1]
We will discuss Complex Networks and SNA in next posts.
see u again
Ins Network Advisor
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