One of the most dramatic advantages afforded by recent advances in internet technology and sensing technology is the ability to persistently survey one or several domain(s) in land, air, sea, space and

even cyber space, resulting in a preponderance of all sorts of information, which can then be accessed

almost instantly across the globe via the internet. However, this enrichment of data comes at a

cost. First of all, the sheer volume currently overwhelms even the best algorithms and systems

that had been developed to process these types of data. Furthermore, not all of these data are

good or equally important - some data are raw in the sense that there are much uncertainty and

misalignment in them; and other data are just redundant and/or can be considered as noise that

obfuscate the massive data rending them not well understood. In this information age that we

live in, there is, therefore, a growing need to respond to the challenges to make sense of all these

data, often modeled by large-scale graphs that represent various communication networks, sensor

networks, social networks, etc.

In response to such challenges, we herein propose a framework using advanced tools from random graph theory and spectral graph theory by which to carry out the quantitative

analysis of the structure and dynamics of large networks..... More coming...