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Community structure is one of the most important properties of networks. Mostcommunity algorithms are not suitable for large networks because of their timeconsuming. In fact there are lots of networks with millons even billons ofnodes. In such case, most algorithms running in time O(n2logn) or even largerare not practical. What we need are linear or approximately linear timealgorithm. Rising in response to such needs, we propose a quick methods toevaluate community structure in networks and then put forward a local communityalgorithm with nearly linear time based on random walks. Using our communityevaluating measure, we could find some difference results from measures usedbefore, i.e., the Newman Modularity. Our algorithm are effective in smallbenchmark networks with small less accuracy than more complex algorithms but agreat of advantage in time consuming for large networks, especially super largenetworks.

Evaluating community structure in large network with random walks