In recent years,large-scale graph analysis has played an important role in big data computing.The classical graph analysis problems include computing the graph diameter,the radius,the girth,the clustering coefficient and various centrality indices.To solve these problems,centralized algorithms generally require square or even cubic time complexity,which is obviously not applicable to large-scale graphs.In this paper,we aim to briefly review some low complexity (linear time) algorithms for these basic graph problems from a perspective of distributed algorithms.In addition,this paper also shows how to prove the lower bound of distributed graph computing by utilizing the communication complexity theory.