Network Analysis

Why networks?

Individual expression could be thought as a phenomenon regulated mostly by the individual, but in a second stand it is also modified by the interactions with the surroundings.  Can the response of the individual be predicted by the group? (See the following video of an experiment conducted by Asch


 Most common type of network analysis

  • Basic network summary statistics (description)
  • Clustering methods (Extract information)
  • Random graphs (Description, inference and to model network topology)
  • Learning machine methods (Prediction)

Random Graphs and the topology structure

Depending on the structure of a desired network different random models could be of use, for example, if the goal is to obtain a sparse and not highly connected network then an ER model could be of use (this model randomly assign the edges between nodes)
or if the goal is exactly the opposite (have a very highly connected network) a geometric graph could be of use (this model randomly assign positions in a n-dimensional space and then place edges between nodes closer than a given distance). 

Is there already a random model?

According to our recent results we suspect there is no null model yet for PPIs, even though  for some virus PPIs some of the random models seem to be very good models; however this virus PPIs are much smaller (around 300 nodes and up to 500 edges) than the networks of model organisms (usually with more than 2000 nodes and 5000 edges) such as yeast, human, fruit fly and Escherichia coli among others.
We will soon be publishing our article with details about this.

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