Network as a computer: ranking paths to find flows
Abstract
We explore a simple mathematical model of network computation, based on Markov chains. Similar models apply to a broad range of computational phenomena, arising in networks of computers, as well as in genetic, and neural nets, in social networks, and so on. The main problem of interaction with such spontaneously evolving computational systems is that the data are not uniformly structured. An interesting approach is to try to extract the semantical content of the data from their distribution among the nodes. A concept is then identified by finding the community of nodes that share it. The task of data structuring is thus reduced to the task of finding the network communities, as groups of nodes that together perform some nonlocal data processing. Towards this goal, we extend the ranking methods from nodes to paths. This allows us to extract some information about the likely flow biases from the available static information about the network.
 Publication:

arXiv eprints
 Pub Date:
 February 2008
 arXiv:
 arXiv:0802.1306
 Bibcode:
 2008arXiv0802.1306P
 Keywords:

 Computer Science  Information Retrieval;
 Computer Science  Artificial Intelligence;
 Mathematics  Category Theory;
 H.3.3;
 I.2.4;
 I.2.6
 EPrint:
 12 pages, CSR 2008