# Efficient Algorithms for Complex Networks

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.

Many such examples exist in the literature. For instance, the arboricity and core value have been used to enumerate $$k$$-cliques. However I am not aware on any survey devoted to them.