  The loop structure plays an important role in many aspects of complex
networks and attracts much attention. Among the previous works, Bianconi et al
find that real networks often have fewer short loops as compared to random
models. In this paper, we focus on the uneven location of loops which makes
some parts of the network rich while some other parts sparse in loops. We
propose a node removing process to analyze the unevenness and find rich loop
cores can exist in many real networks such as neural networks and food web
networks. Finally, an index is presented to quantify the unevenness of loop
location in complex networks.
