Complex Network Analysis
 
Objective:

To develop, a multi-paradigm network modeling framework, together with a characterization of tradeoffs between speed and accuracy for multiple modeling approaches, as a function of different types and scales of networks, protocols, traffic and application types, and metrics. The network properties to be studied include performance (measured in latency, throughput, and related application or protocol-specific properties) of global network configurations, as well as the stability properties of the network, including fault localization and recovery and sacability.

Approach:

This research will leverage recent advances in graph theory and distrubuted computation, and model reduction techniques to devise a unified framework for efficient analysis of large-scale networks. The goal is to use a combination of analytical models together with abstract models inorder to obtain efficiency with known bounds on the inaccuracy introduced in the system.

The design of detailed and abstract models in a common framework will provide a direct opportunity for validation of abstract models. Further, detailed measurements will be used for qualitative validation of trends as well as development of error bounds.

Recent statistical analysis on empirical data has shown strong indication of the self-similar nature of network traffic. Measurement data from web transfers showed that web traffic distribution has an upper tail which declines like a power law with exponent close to 1, i.e., a heavy tail. Heavy-tailed distributions are common in many complex bio-/eco-/techno-/socio-logical systems. Recently, a radically different theory for the nature of complexity and the origin of power laws and phase transitions in complex systems is found and the eventual goal is to produce a more unified treatment of information, control, and computation, having found that the standard theories are too fragmented and brittle to provide a foundation for more systematic design of network protocols.

Implementation:

In this research we use the open source crawler software, Nutch, in order to generate the graph structure of the WWW network. In order to obtain such a graph structure, we start from the DMOZ directory and politely crawl through the hyper links in those pages in order to form the graph structure.

Based on www.robotstxt.org regulations, we honor both robots.txt and HTML META TAG format. According to this regulation, if none of these standards are not set, by default it means we can crawl those pages. Our crawler introduces itself like as:

http.agent.name = complex_network_group
http.agent.description = discovering the structure of the world-wide-web
http.agent.url = http://cantor.ee.ucla.edu/~networks/
http.agent.email = nimakh@ee.ucla.edu

So, we should not be considered as spam or denial service attack (DOS). Please let us know if you have any issue regarding this research program please email vwani@ee.ucla.edu