Most standard works on random graphs focus on $G_{n,p}$ and random regular graphs. However, such models are far from a good abstraction to describe the types of networks that one typically encounters in the real world.
There are several simple models that mimic the behavior of real-world graphs, sometimes called Complex Networks.
There are many graph problems that become easier in the setting of $G_{n,p}$, such as finding a Hamiltonian path, finding a perfect matching, etc. I am sure that similar work has been done for random complex networks, but I haven't been able to find a good book or survey paper on the subject. I must be using the wrong terminology.
I would be grateful if someone could point me towards something like that, or tell me what the subject of efficient algorithms for complex networks is called in the literature.