Community evolutionary pattern analysis in temporal datasets is a key issue in social network dynamics research and applications. Identifying outlying objects against main community evolution trends is not only meaningful by itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. Upon giving the belonging matrix of community members, this study defines a type of transition matrix to characterize the pattern of the evolutionary dynamic between two consecutive belonging snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the gradual community structural change in quantity. The transition matrix is further optimized using M-estimator Robust Regression methods by minimizing the disturbance incurred by the outliers, and the abnormality of the outlier objects can then be computed at the same time. Considering that large proportion of trivial but nomadic objects may exist in large datasets like those of complex social networks, focus is placed only on the community evolutionary outliers that show remarkable difference from the main bodies of their communities and sharp change of their membership role within the communities. A definition on such type of local and global outliers is given, and an algorithm on detection such kind of outliers is proposed in this paper. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting evolutionary community outliers.