An Analysis of Content Sharing Hops for Dual-Structural Network Based on General Random Graph

Dual-Structural Network (DSN), pioneered by our group for content sharing, is a networking paradigm with the Internet as primary structure and the broadcast-storage network (BSN) as secondary structure. In order to quantitatively evaluate its content sharing capability, in this paper, we generally adopt a deductive methodology, namely that DSN is formalized as a complex network and then its sharing capability is derived according to graph theory. In specific, according to bipartite graph theory, we first construct a bipartite dual-structural network model to obtain an abstract content sharing graph through top-down projection, and then content sharing hops (CSH) in the graph is capitalized as a metric to evaluate the sharing capability between any two content nodes. Furthermore, we leverage the general random graph theory to generate the sharing graph for deriving quantitative upper bounds on average content sharing hops (ACSH) and maximum content sharing hops (MCSH) of DSN. Lastly, the theoretical derivations are validated by numerical simulation. Moreover, compared with content delivery network (CDN), content centric network (CCN) and information-centric mobile ad hoc networks (ICMANET), DSN is demonstrated to be superior in terms of the sharing capability.