Recent results in combinatorial optimization have shown that complex networks can be fruitfully used to modeling problem structure. By conveying results and tools from complex networks to combinatorial optimization, it is possible to achieve a deeper understanding of algorithm behavior. Moreover, some features of the network that models an instance can guide the design of specific heuristics and enable to choose the best solver among a portfolio of algorithms. This work gives a brief overview of the state of the art in this interdisciplinary research field
