A minimal cellular automaton model is introduced to describe the collective motion of self-propelled particles on two-dimensional square lattice. The model features discretization of directional and positional spaces and single-particle occupation on one lattice site. Contrary to Vicsek model and its variants, our model exhibits nonvanishing optimal noise. When particle density increases, the collective motion is promoted with optimal noise strength and reduced with noise strength out of optimal region. In addition, when the square lattice undergoes edge percolation process, no abrupt change of alignment behaviors is observed at the critical point of percolation.

Despite the wide use of networks as a versatile tool for exploring complex social systems, little is known about how to detect and forecast abrupt changes in social systems. In this report, we develop an early warning approach based on network properties to detect such changes. By analysing three collaborative social networks-one co-stardom, one patent and one scientific collaborative network, we discover that abrupt transitions inherent in these networks can serve as a good early warning signal, indicating, respectively, the dissolution of the Soviet Union, the emergence of the "soft matter" research field, and the merging of two scientific communities. We then develop a clique growth model that explains the universal properties of these real networks and find that they 1